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Both are derived from analogy with natural system evolution and both deal with the same kind of optimization problem. GAs differs by two main features, which should make them more efficient. First GAs uses a population-based selection whereas SA only deals with one individual at each iteration.

Hence GAs are expected to cover a much larger landscape of the search space at each iteration, but on the other hand SA iterations are much more simple, and so, often much faster. The great advantage of GA is its exceptional ability to be parallelized, whereas SA does not gain much of this. It is mainly due to the population scheme use by GA. Secondly, GAs uses recombination operators, able to mix good characteristics from different solutions. The exploitation made by recombination operators is supposedly considered helpful to find optimal solutions of the problem.

On the other hand, simulated annealing are still very simple to implement and they give good results. They have proved their efficiency over a large spectrum of difficult problems, like the optimal layout of printed circuit board, or the famous 2. Genetic annealing is developing in the recent years, which is an attempt to combine genetic algorithms and simulated annealing.

Most of them can usually only solve one given specific problem, since their architecture was designed for whatever that specific problem was in the first place. Thus, if the given problem were somehow to be changed, these systems could have a hard time adapting to them, since the algorithm that would originally arrive to the solution may be either incorrect or less efficient. Genetic algorithms or GA were created to combat these problems. They are basically algorithms based on natural biological evolution.

The architecture of systems that implement genetic algorithms or GA is more able to adapt to a wide range of problems. A genetic algorithm is a problem solving method that uses genetics as its model of problem solving. Basically, an optimization problem looks really simple. One knows the form of all possible solutions corresponding to a specific question. The set of all the solutions that meet this form constitute the search space. The problem consists in finding out the solution that fits the best, i.

But, when the search space becomes large, enumeration is soon no longer feasible simply because it would take far too much time. Genetic Algorithms provides one of these methods. Practically they all work in a similar way, adapting the simple genetics to algorithmic mechanisms. GA handles a population of possible solutions. Each solution is represented through a chromosome, which is just an abstract representation.

Coding all the possible solutions into a chromosome is the first part, but certainly not the most straightforward one of a Genetic Algorithm. A set of reproduction operators has to be determined, too. Reproduction operators are applied directly on the chromosomes, and are used to perform mutations and recombinations over solutions of the problem.

Appropriate representation and reproduction operators are really something determinant, as the behavior of the GA is extremely dependant on it. Frequently, it can be extremely difficult to find a representation, which respects the structure of the search space and reproduction operators, which are coherent and relevant according to the properties of the problems.

Selection is done by using a fitness function. Each chromosome has an associated value corresponding to the fitness of the solution it represents. The fitness should correspond to an evaluation of how good the candidate solution is. The optimal solution is the one, which maximizes the fitness function. Genetic Algorithms deal with the problems that maximize the fitness function. But, if the problem consists in minimizing a cost function, the adaptation is quite easy.

Either the cost function can be transformed into a fitness function, for example by inverting it; or the selection can be adapted in such way that they consider individuals with low evaluation functions as better. Once the reproduction and the fitness function have been properly defined, a Genetic Algorithm is evolved according to the same basic structure. It starts by generating an initial population of chromosomes. This first population must offer a wide diversity of genetic materials. The gene pool should be as large as possible so that any solution of the search space can be engendered.

Generally, the initial population is generated randomly. Then, the genetic algorithm loops over an iteration process to make the population evolve. This selection is done randomly with a probability depending on the relative fitness of the individuals so that best ones are often chosen for reproduction than poor ones. For generating new chromosomes, the algorithm can use both recombination and mutation. The algorithm is stopped when the population converges toward the optimal solution.

If no crossover was performed, offspring is the exact copy of parents. The Genetic algorithm process is discussed through the GA cycle in Fig. Mutation is performed to one individual to produce a new version of it where some of the original genetic material has been randomly changed.

Selection process helps to decide which individuals are to be used for reproduction and mutation in order to produce new search points. The flowchart showing the process of GA is as shown in Fig. Before implementing GAs it is important to understand few guidelines for designing a general search algorithm i. This can be avoided, but it is a well-known fact that the observation of the worst-case situation is not guaranteed to be possible in general. It is therefore likely that a search method should be stochastic, but it may well contain a substantial portion of determinism, however.

In principle it is enough to have as much nondeterminism as to be able to avoid the worst-case wolf traps. No Reproduce and ignore few populations Perform mutation Fig. It is therefore reasonable to do as much as possible efficient deterministic predictions of the most promising directions of local proceedings.

This is called local hill climbing or greedy search according to the obvious strategies. Based on the foregoing discussion, the important criteria for GA approach can be formulated as given below: - Completeness: Any solution should have its encoding - Non redundancy: Codes and solutions should correspond one to one - Soundness: Any code produced by genetic operators should have its corresponding solution - Characteristic perseverance: Offspring should inherit useful characteristics from parents.

The representation of the problem The fitness calculation Various variables and parameters involved in controlling the algorithm The representation of result and the way of terminating the algorithm 2. Sometimes, when the problem is naturally two or three-dimensional also corresponding array structures are used. A set, called population, of these problem dependent parameter value vectors is processed by GA. To start there is usually a totally random population, the values of different parameters generated by a random number generator.

Typical population size is from few dozens to thousands. To do optimization we need a cost function or fitness function as it is usually called when genetic algorithms are used. By a fitness function we can select the best solution candidates from the population and delete the not so good specimens.

The nice thing when comparing GAs to other optimization methods is that the fitness function can be nearly anything that can be evaluated by a computer or even something that cannot! In the latter case it might be a human judgement that cannot be stated as a crisp program, like in the case of eyewitness, where a human being selects among the alternatives generated by GA.

So, there are not any definite mathematical restrictions on the properties of the fitness function. It may be discrete, multimodal etc. There is a clear difference between discrete and continuous problems. Therefore it is instructive to notice that continuous methods are sometimes used to solve inherently discrete problems and vice versa.

Parallel algorithms are usually used to speed up processing. There are, however, some cases in which it is more efficient to run several processors in parallel rather than sequentially. These cases include among others such, in which there is high probability of each individual search run to get stuck into a local extreme. Irrespective of the above classification, optimization methods can be further classified into deterministic and non-deterministic methods.

In addition optimization algorithms can be classified as local or global. In terms of energy and entropy local search corresponds to entropy while global optimization depends essentially on the fitness i. GAs operate with coded versions of the problem parameters rather than parameters themselves i.

Almost all conventional optimization techniques search from a single point but GAs always operate on a whole population of points strings i. This plays a major role to the robustness of genetic algorithms. It improves the chance of reaching the global optimum and also helps in avoiding local stationary point. GA uses fitness function for evaluation rather than derivatives. As a result, they can be applied to any kind of continuous or discrete optimization problem.

The key point to be performed here is to identify and specify a meaningful decoding function. GAs use probabilistic transition operates while conventional methods for continuous optimization apply deterministic transition operates i.

These are the major differences that exist between Genetic Algorithm and conventional optimization techniques. Parallelism 2. Liability 3. Solution space is wider 4. The fitness landscape is complex 5. Easy to discover global optimum 6. The problem has multi objective function 7. Only uses function evaluations. Easily modified for different problems.

Handles noisy functions well. Handles large, poorly understood search spaces easily Good for multi-modal problems Returns a suite of solutions. Very robust to difficulties in the evaluation of the objective function. They require no knowledge or gradient information about the response surface Discontinuities present on the response surface have little effect on overall optimization performance They are resistant to becoming trapped in local optima 2.

They perform very well for large-scale optimization problems Can be employed for a wide variety of optimization problems The limitation of genetic algorithm includes, 1. The problem of identifying fitness function 2. Definition of representation for the problem 3.

Premature convergence occurs 4. The problem of choosing the various parameters like the size of the population, mutation rate, cross over rate, the selection method and its strength. Cannot use gradients. Cannot easily incorporate problem specific information 7. Not good at identifying local optima 8. No effective terminator. Not effective for smooth unimodal functions Needs to be coupled with a local search technique. Have trouble finding the exact global optimum Require large number of response fitness function evaluations Configuration is not straightforward 2.

They have been also used for some art, for evolving pictures and music. The method is very different from classical optimization algorithms. Use of the encoding of the parameters, not the parameters themselves. Work on a population of points, not a unique one. Use the only values of the function to optimize, not their derived function or other auxiliary knowledge. Use probabilistic transition function not determinist ones.

The problem is in a stochastic system and a genetic pool may be too far from the solution, or for example, a too fast convergence may halt the process of evolution. These algorithms are nevertheless extremely efficient, and are used in fields as diverse as stock exchange, production scheduling or programming of assembly robots in the automotive industry.

GAs can even be faster in finding global maxima than conventional methods, in particular when derivatives provide misleading information. It should be noted that in most cases where conventional methods can be applied, GAs are much slower because they do not take auxiliary information like derivatives into account. In these optimization problems, there is no need to apply a GA, which gives less accurate solutions after much longer computation time.

The enormous potential of GAs lies elsewhere—in optimization of non-differentiable or even discontinuous functions, discrete optimization, and program induction. It has been claimed that via the operations of selection, crossover, and mutation the GA will converge over successive generations towards the global or near global optimum.

This simple operation should produces a fast, useful and robust technique largely because of the fact that GAs combine direction and chance in the search in an effective and efficient manner. Since population implicitly contain much more information than simply the individual fitness scores, GAs combine the good information hidden in a solution with good information from another solution to produce new solutions with good information inherited from both parents, inevitably hopefully leading towards optimality.

The ability of the algorithm to explore and exploit simultaneously, a growing amount of theoretical justification, and successful application to real-world problems strengthens the conclusion that GAs are a powerful, robust optimization technique.

Brief the origin of Genetic Algorithm. Discuss in detail about the biological process of natural evolution. Compare the terminologies of natural evolution and Genetic Algorithm. Define: Search space. Describe about various conventional optimization and search techniques. Write short note on simple Genetic Algorithm.

Compare and contrast Genetic Algorithm with other optimization techniques. State few advantages and disadvantages of Genetic Algorithm. Mention certain applications of Genetic Algorithm. In genetic algorithms, individuals are binary digits or of some other set of symbols drawn from a finite set. As computer memory is made up of array of bits, anything can be stored in a computer and can also be encoded by a bit string of sufficient length.

Each of the encoded individual in the population can be viewed as a representation, according to an appropriate encoding of a particular solution to the problem. For Genetic Algorithms to find a best optimum solution, it is necessary to perform certain operations over these individuals. This chapter discusses the basic terminologies and operators used in Genetic Algorithms to achieve a good enough solution for possible terminating conditions.

An individual is a single solution while the population is the set of individuals currently involved in the search process. Individual groups together two forms of solutions as given below: 1. The phenotype, which is the expressive of the chromosome in the terms of the model. Factor N Gene 1 Gene 2 Gene 3 …………. Gene N Chromosome Genotype Fig. Each factor in the solution set corresponds to gene in the chromosome. Figure 3. A chromosome should in some way contain information about solution that it represents.

The morphogenesis function associates each genotype with its phenotype. It simply means that each chromosome must define one unique solution, but it does not mean that each solution encoded by exactly one chromosome. Indeed, the morphogenesis function is not necessary bijective, and it is even sometimes impossible especially with binary representation. Nevertheless, the morphogenesis function should at least be subjective. Indeed; all the candidate solutions of the problem must correspond to at least one possible chromosome, to be sure that the whole search space can be explored.

When the morphogenesis function that associates each chromosome to one solution is not injective, i. A slight degeneracy is not so worrying, even if the space where the algorithm is looking for the optimal solution is inevitably enlarged. But a too important degeneracy could be a more serious problem. It can badly affect the behavior of the GA, mostly because if several chromosomes can represent the same phenotype, the meaning of each gene will obviously not correspond to a specific characteristic of the solution.

It may add some kind of confusion in the search. Chromosomes are encoded by bit strings are given below in Fig. A chromosome is a sequence of genes. Genes may describe a possible solution to a problem, without actually being the solution. A gene is a bit string of arbitrary lengths. The bit string is a binary representation of number of intervals from a lower bound. This range can be divided 3. The structure of each gene is defined in a record of phenotyping parameters. The phenotype parameters are instructions for mapping between genotype and phenotype.

It can also be said as encoding a solution set into a chromosome and decoding a chromosome to a solution set. The mapping between genotype and phenotype is necessary to convert solution sets from the model into a form that the GA can work with, and for converting new individuals from the GA into a form that the model can evaluate. In a chromosome, the genes are represented as in Fig.

For calculating fitness, the chromosome has to be first decoded and the objective function has to be evaluated. The fitness not only indicates how good the solution is, but also corresponds to how close the chromosome is to the optimal one. In the case of multicriterion optimization, the fitness function is definitely more difficult to determine.

In multicriterion optimization problems, there is often a dilemma as how to determine if one solution is better than another. What should be done if a solution is better for one criterion but worse for another? If sometimes a fitness function obtained by a simple combination of the different criteria can give good result, it suppose that criterions can be combined in a consistent way.

But, for more advanced problems, it may be useful to consider something like Pareto optimally or others ideas from multicriteria optimization theory. A population consists of a number of individuals being tested, the phenotype parameters defining the individuals and some information about search space. The two important aspects of population used in Genetic Algorithms are: 1.

The initial population generation. The population size. It is often a random initialization of population is carried. In the case of a binary coded chromosome this means, that each bit is initialized to a random zero or one. But there may be instances where the initialization of population is carried out with some known good solutions. Ideally, the first population should have a gene pool as large as possible in order to be able to explore the whole search space.

All the different possible alleles of each should be present in the population. To achieve this, the initial population is, in most of the cases, chosen randomly. Nevertheless, sometimes a kind of heuristic can be used to seed the initial population. Thus, the mean fitness of the population is already high and it may help the genetic algorithm to find good solutions faster. But for doing this one should be sure that the gene pool is still large enough.

Otherwise, if the population badly lacks diversity, the algorithm will just explore a small part of the search space and never find global optimal solutions. The size of the population raises few problems too. The larger the population is, the easier it is to explore the search space.

But it has established that the time required by a GA to converge is O nlogn function evaluations where n is the population size. We say that the population has converged when all the individuals are very much alike and further improvement may only be possibly by mutation. Goldberg has also shown that GA efficiency to reach global optimum instead of local ones is largely determined by the size of the population.

To sum up, a large population is quite useful. But it requires much more computational cost, memory and time. Practically, a population size of around individuals is quite frequent, but anyway this size can be changed according to the time and the memory disposed on the machine compared to the quality of the result to be reached.

Population being combination of various chromosomes is represented as in Fig. An entire chromosome population can be stored in a single array given the number of individuals and the length of their genotype representation. Similarly, the design variables, or phenotypes that are 3.

The actual mapping depends upon the decoding scheme used. The objective function values can be scalar or vectorial and are necessarily the same as the fitness values. Fitness values are derived from the object function using scaling or ranking function and can be stored as vectors. There can be several goals for the search process, one of which is to find the global optima. This can never be assured with the types of models that GAs work with. There is always a possibility that the next iteration in the search would produce a better solution.

In some cases, the search process could run for years and does not produce any better solution than it did in the first little iteration. Another goal is faster convergence. When the objective function is expensive to run, faster convergence is desirable, however, the chance of converging on local, and possibly quite substandard optima is increased.

Apart from these, yet another goal is to produce a range of diverse, but still good solutions. When the solution space contains several distinct optima, which are similar in fitness, it is useful to be able to select between them, since some combinations of factor values in the model may be more feasible than others.

Also, some solutions may be more robust than others. The process can be performed using bits, numbers, trees, arrays, lists or any other objects. The encoding depends mainly on solving the problem. For example, one can encode directly real or integer numbers. Each bit in the string can represent some characteristics of the solution. Every bit string therefore is a solution but not necessarily the best solution.

The way bit strings can code differs from problem to problem. Binary encoding gives many possible chromosomes with a smaller number of alleles. On the other hand this encoding is not natural for many problems and sometimes corrections must be made after genetic operation is completed.

Binary coded strings with 1s and 0s are mostly used. The length of the string depends on the accuracy. Chromosome 1 Chromosome 2 Fig. Sometimes corrections have to be done after genetic operation is completed. Even for this problems for some types of crossover and mutation corrections must be made to leave the chromosome consistent i. This encoding produces best results for some special problems.

Direct value encoding can be used in problems, where some complicated values, such as real numbers, are used. Use of binary encoding for this type of problems would be very difficult. In value encoding, every chromosome is a string of some values. Values can be anything connected to problem, form numbers, real numbers or chars to some complicated objects. Chromosome A 1. On the other hand, for this encoding is often necessary to develop some new crossover and mutation specific for the problem.

Every chromosome is a tree of some objects such as functions and commands of a programming language. It is in this process, the search process creates new and hopefully fitter individuals. The breeding cycle consists of three steps: a.

Selecting parents. Crossing the parents to create new individuals offspring or children. Replacing old individuals in the population with the new ones. After deciding on an encoding, the next step is to decide how to perform selection i.

The purpose of selection is to emphasize fitter individuals in the population in hopes that their off springs have higher fitness. Chromosomes are selected from the initial population to be parents for reproduction. The problem is how to select these chromosomes. The Fig. Selection is a method that randomly picks chromosomes out of the population according to their evaluation function. The higher the fitness function, the more chance an individual has to be selected.

The selection pressure is defined as the degree to which the better individuals are favored. The higher the selection pressured, the more the better individuals are favored. This selection pressure drives the GA to improve the population fitness over the successive generations. The convergence rate of GA is largely determined by the magnitude of the selection pressure, with higher selection pressures resulting in higher convergence rates.

The two best individuals Mating Pool Fig. However, if the selection pressure is too low, the convergence rate will be slow, and the GA will take unnecessarily longer time to find the optimal solution. If the selection pressure is too high, there is an increased change of the GA prematurely converging to an incorrect sub-optimal solution.

In addition to providing selection pressure, selection schemes should also preserve population diversity, as this helps to avoid premature convergence. Typically we can distinguish two types of selection scheme, proportionate selection and ordinal-based selection.

Proportionate-based selection picks out individuals based upon their fitness values relative to the fitness of the other individuals in the population. Ordinal-based selection schemes selects individuals not upon their raw fitness, but upon their rank within the population.

This requires that the selection pressure is independent of the fitness distribution of the population, and is solely based upon the relative ordering ranking of the population. It is also possible to use a scaling function to redistribute the fitness range of the population in order to adapt the selection pressure. For example, if all the solutions have their fitness in the range [, ], the probability of selecting a better individual than any other using a proportionate-based method will not be important.

If the fitness in every individual is brought to the range [0, 1] equitably, the probability of selecting good individual instead of bad one will be important. Selection has to be balanced with variation form crossover and mutation. Too strong selection means sub optimal highly fit individuals will take over the population, reducing the diversity needed for change and progress; too weak selection will result in too slow evolution. The various selection methods are discussed as follows: 3.

The commonly used reproduction operator is the proportionate reproductive operator where a string is selected from the mating pool with a probability proportional to the fitness. A target value is set, which is a random proportion of the sum of the fit nesses in the population. The population is stepped through until the target value is reached. This is only a moderately strong selection technique, since fit individuals are not guaranteed to be selected for, but somewhat have a greater chance.

A fit individual will contribute more to the target value, but if it does not exceed it, the next chromosome in line has a chance, and it may be weak. It is essential that the population not be sorted by fitness, since this would dramatically bias the selection. The above described Roulette process can also be explained as follows: The expected value of an individual is that fitness divided by the actual fitness of the population.

The wheel is spun N times, where N is the number of individuals in the population. Sum the total expected value of the individuals in the population. Let it be T. Repeat N times: i. The individual whose expected value puts the sum over this limit is the one selected. Roulette wheel selection is easier to implement but is noisy. In terms of disruption of genetic codes, random selection is a little more disruptive, on average, than roulette wheel selection.

Rank Selection ranks the population and every chromosome receives fitness from the ranking. The worst has fitness 1 and the best has fitness N. It results in slow convergence but prevents too quick convergence.

It also keeps up selection pressure when the fitness variance is low. It preserves diversity and hence leads to a successful search. In effect, potential parents are selected and a tournament is held to decide which of the individuals will be the parent.

There are many ways this can be achieved and two suggestions are, 1. Select a pair of individuals at random. Generate a random number, R, between 0 and 1. This is repeated to select the second parent. The value of r is a parameter to this method. Select two individuals at random. The individual with the highest evaluation becomes the parent.

Repeat to find a second parent. Unlike, the Roulette wheel selection, the tournament selection strategy provides selective pressure by holding a tournament competition among Nu individuals. Tournament competitions and the winner are then inserted into the mating pool. The tournament competition is repeated until the mating pool for generating new offspring is filled. The mating pool comprising of the tournament winner has higher average population fitness. The fitness difference provides the selection pressure, which drives GA to improve the fitness of the succeeding genes.

This method is more efficient and leads to an optimal solution. This method simulates the process of slow cooling of molten metal to achieve the minimum function value in a minimization problem. Controlling a temperature like parameter introduced with the concept of Boltzmann probability distribution simulates the cooling phenomenon.

In Boltzmann selection a continuously varying temperature controls the rate of selection according to a preset schedule. The temperature starts out high, which means the selection pressure is low. The temperature is gradually lowered, which gradually increases the selection pressure, thereby allowing the GA to narrow in more closely to the best part of the search space while maintaining the appropriate degree of diversity.

A logarithmically decreasing temperature is found useful for convergence without getting stuck to a local minima state. But to cool down the system to the equilibrium state takes time. Let fmax be the fitness of the currently available best string. The final state is reached when computation approaches zero value of T, i. The probability that the best string is selected and introduced into the mating pool is very high. However, Elitism can be used to eliminate the chance of any undesired loss of information during the mutation stage.

Moreover, the execution time is less. Elitism The first best chromosome or the few best chromosomes are copied to the new population. The rest is done in a classical way. Such individuals can be lost if they are not selected to reproduce or if crossover or mutation destroys them.

Here equally spaced pointers are placed over the line, as many as there are individuals to be selected. Sample of 1 random number in the range [0, 0. After selection the mating population consists of the individuals, 1, 2, 3, 4, 6, 8. Stochastic universal sampling ensures a selection of offspring, which is closer to what is deserved than roulette wheel selection. After the selection reproduction process, the population is enriched with better individuals.

Reproduction makes clones of good strings but does not create new ones. Crossover operator is applied to the mating pool with the hope that it creates a better offspring. Crossover is a recombination operator that proceeds in three steps: i. The reproduction operator selects at random a pair of two individual strings for the mating. A cross site is selected at random along the string length. Finally, the position values are swapped between the two strings following the cross site.

The various crossover techniques are discussed as follows: 3. Here, a cross-site or crossover point is selected randomly along the length of the mated strings and bits next to the cross-sites are exchanged. If appropriate site is chosen, better children can be obtained by combining good parents else it severely hampers string quality.

The above Fig. The crossover point can be chosen randomly. It should be noted that adding further crossover points reduces the performance of the GA. The problem with adding additional crossover points is that building blocks are more likely to be disrupted. However, an advantage of having more crossover points is that the problem space may be searched more thoroughly. In two-point crossover, two crossover points are chosen and the contents between these points are exchanged between two mated parents.

Thus the contents between these points are exchanged between the parents to produce new children for mating in the next generation. Originally, GAs were using one-point crossover which cuts two chromosomes in one point and splices the two halves to create new ones. But with this one-point crossover, the head and the tail of one chromosome cannot be passed together to the offspring.

If both the head and the tail of a chromosome contain good genetic information, none of the offsprings obtained directly with one-point crossover will share the two good features. Using a 2-point crossover avoids this drawback, and then, is generally considered better than 1-point crossover. In fact this problem can be generalized to each gene position in a chromosome. Genes that are close on a chromosome have more chance to be passed together to the offspring obtained through a N-points crossover.

It leads to an unwanted correlation between genes next to each other. Consequently, the efficiency of a N-point crossover will depend on the position of the genes within the chromosome. In a genetic representation, genes that encode dependant characteristics of the solution should be close together.

To avoid all the problem of genes locus, a good thing is to use a uniform crossover as recombination operator. One is even number of cross-sites and the other odd number of cross-sites. In the case of even number of cross-sites, cross-sites are selected randomly around a circle and information is exchanged. In the case of odd number of cross-sites, a different cross-point is always assumed at the string beginning. Each gene in the offspring is created by copying the corresponding gene from one or the other parent 3.

Where there is a 1 in the crossover mask, the gene is copied from the first parent, and where there is a 0 in the mask the gene is copied from the second parent. A new crossover mask is randomly generated for each pair of parents. Offsprings, therefore contain a mixture of genes from each parent. In Fig. It can be noticed, that while producing child 1, when there is a 1 in the mask, the gene is copied from the parent 1 else from the parent 2.

On producing child 2, when there is a 1 in the mask, the gene is copied from parent 2, when there is a 0 in the mask; the gene is copied from the parent 1. Each bit of the first parent is compared with the bit of the second parent.

If both are the same, the bit is taken for the offspring otherwise; the bit from the third parent is taken for the offspring. This concept is illustrated in Fig. This is implemented by restricting the location of crossover points such that crossover points only occur where gene values differ. A single crossover position as in single-point crossover is selected.

But before the variables are exchanged, they are randomly shuffled in both parents. After recombination, the variables in the offspring are unshuffled. This removes positional bias as the variables are randomly reassigned each time crossover is performed. The operator passes on precedence relations of operations given in two parental permutations to one offspring at the same rate, while no new precedence relations are introduced.

PPX is illustrated in below, for a problem consisting of six operations A—F. Example is shown in Fig. Given two parent chromosomes, two random crossover points are selected partitioning them into a left, middle and right portion. The ordered two-point crossover behaves in the following way: child 1 inherits its left and right section from parent 1, and its middle section is determined Fig.

A similar process is applied to determine child 2. This is shown in Fig. Indeed, TSP chromosomes are simply sequences of integers, where each integer represents a different city and the order represents the time at which a city is visited. Under this representation, known as permutation encoding, we are only interested in labels and not alleles. It may be viewed as a crossover of permutations that guarantees that all positions are found exactly once in each offspring, i.

PMX proceeds as follows: 1. The two chromosomes are aligned. Crossover probability is a parameter to describe how often crossover will be performed. If there is no crossover, offspring are exact copies of parents. Crossover is made in hope that new chromosomes will contain good parts of old chromosomes and therefore the new chromosomes will be better. However, it is good to leave some part of old population survive to next generation.

Mutation prevents the algorithm to be trapped in a local minimum. Mutation plays the role of recovering the lost genetic materials as well as for randomly disturbing genetic information. It is an insurance policy against the irreversible loss of genetic material. Mutation has traditionally considered as a simple search operator. If crossover is supposed to exploit the current solution to find better ones, mutation is supposed to help for the exploration of the whole search space.

Mutation is viewed as a background operator to maintain genetic diversity in the population. It introduces new genetic structures in the population by randomly modifying some of its building blocks. It also keeps the gene pool well stocked, and thus ensuring ergodicity. A search space is said to be ergodic if there is a non-zero probability of generating any solution from any population state. There are many different forms of mutation for the different kinds of representation.

For binary representation, a simple mutation can consist in inverting the value of each gene with a small probability. It is also possible to implement kind of hill-climbing mutation operators that do mutation only if it improves the quality of the solution. Such an operator can accelerate the search.

But care should be taken, because it might also reduce the diversity in the population and makes the algorithm converge toward some local optima. Mutation of a bit involves flipping a bit, changing 0 to 1 and vice-versa. A parent is considered and a mutation chromosome is randomly generated.

For a 1 in mutation chromosome, the corresponding bit in parent chromosome is flipped 0 to 1 and 1 to 0 and child chromosome is produced. In the above case, there occurs 1 at 3 places of mutation chromosome, the corresponding bits in parent chromosome are flipped and child is generated.

The mutation probability decides how often parts of chromosome will be mutated. If there is no mutation, offspring are generated immediately after crossover or directly copied without any change. If mutation is performed, one or more parts of a chromosome are changed. Mutation generally prevents the GA from falling into local extremes. Mutation should not occur very often, because then GA will in fact change to random search.

Two parents are drawn from a fixed size population, they breed two children, but not all four can return to the Fig. The technique used to decide which individual stay in a population and which are replaced in on a par with the selection in influencing convergence. Basically, there are two kinds of methods for maintaining the population; generational updates and steady state updates. The basic generational update scheme consists in producing N children from a population of size N to form the population at the next time step generation , and this new population of children completely replaces the parent selection.

Clearly this kind of update implies that an individual can only reproduce with individuals from the same generation. In a steady state update, new individuals are inserted in the population as soon as they are created, as opposed to the generational update where an entire new generation is produced at each time step. The insertion of a new individual usually necessitates the replacement of another population member. The individual to be deleted can be chosen as the worst member of the population.

Tournament replacement is exactly analogous to tournament selection except the less good solutions are picked more often than the good ones. A subtile alternative is to replace the most similar member in the existing population. The parents are also candidates for selection. This can be useful for continuing the search in small populations, since weak individuals can be introduced into the population. With the four individuals only the fittest two, parent or child, return to population.

This process improves the overall fitness of the population when paired with a selection technique that selects both fit and weak parents for crossing, but if weak individuals and discriminated against in selection the opportunity will never raise to replace them. The child replaces the parent. In this case, each individual only gets to breed once. As a result, the population and genetic material moves around but leads to a problem when combined with a selection technique that strongly favors fit parents: the fit breed and then are disposed of.

Note: If the maximum number of generation has been reached before the specified time has elapsed, the process will end. Note: If the maximum number of generation has been reached before the specified number of generation with no changes has been reached, the process will end. The termination or convergence criterion finally brings the search to a halt. The following are the few methods of termination techniques. This brings the search to a faster conclusion guaranteeing at least one good solution.

This guarantees the entire population to be of minimum standard, although the best individual may not be significantly better than the worst. In this case, a stringent convergence value may never be met, in which case the search will terminate after the maximum has been exceeded.

This guarantees that virtually all individuals in the population will be within a particular fitness range, although it is better to pair this convergence criteria with weakest gene replacement, otherwise a few unfit individuals in the population will blow out the fitness sum.

The population size has to be considered while setting the convergence value. A schema is defined as templates for describing a subset of chromosomes with similar sections. The schemata consist of bits 0, 1 and meta-character. The template is a suitable way of describing similarities among Patterns in the chromosomes Holland derived an expression that predicts the number of copies of a particular schema would have in the next generation after undergoing exploitation, crossover and mutation.

It should be noted that particularly good schemata will propagate in future generations. Thus, schema that are low-order, well defined and have above average fitness are preferred and are termed building blocks. This leads to a building block principle of GA: low order, well-defined, average fitness schemata will combine through crossover to form high order, above average fitness schemata.

Since GAs process may schemata in a given generation they are said to have the property of implicit parallelism. A genetic algorithm seeks near-optimal performance through the juxtaposition of short, low-order, high-performance schemata, called the building blocks. The most obvious interpretation is that a schema is highly fit if its average fitness considerably higher than the average fitness of all strings in the search space. For example, suppose that the string length is and that the defining length and the order of the schema is Then the schema will contain points.

First, suppose that every string in the schema except one has relatively low fitness. The single point has very high fitness so that the average schema fitness relative to the search space is high. Then any randomly chosen finite population is highly likely to never see the high fitness point, and so the schema will be very likely to disappear in a few generations.

Similarly, one can choose most points to have high fitness, with a few points having sufficiently low fitness that the schema fitness relative to the whole population is low. Then of course, this low-fitness schema will probably grow and may lead to a good solution.

It is easy to construct less extreme examples. Another interpretation is that a schema is highly fit if the average fitness of the schema representatives in the populations of the GA run is higher than the average fitness of all individuals in these populations. For each trap function, the all-zeros string is a global optimum. The schemata that correspond to these strings are the building blocks.

If the population size is sufficiently large, then the initial population will contain strings that are in the building block schemata, but it is unlikely for a string to be in very many building block schemata. If the population size is large enough, the GA with one-point crossover will be able to find the global optimum.

If the building block hypothesis is a good explanation of why a GA works on a particular problem, then this suggests that crossover should be designed so that it will not be too disruptive of building blocks, but it needs to be present in order to 62 3 Terminologies and Operators of GA combine building blocks.

Thus, knowledge of the configuration of potential building blocks is important in the design of the appropriate crossover. If the building blocks tend to be contiguous on the string, then one-point crossover is most appropriate.

If building blocks are distributed arbitrarily over the string, the GA will not be successful unless the building blocks are identified, either before running the GA or as part of running the GA. Macromutation is mutation of many bits rather than just 1 or 2 as is most likely under standard bitwise mutation. The macrosmutation operator that would be similar to one-point or two-point crossover would be to pick a contiguous sequence of positions and then to replace them with a random string.

For example, suppose that this kind of macromutation is applied to string x. One choose a contiguous segment of x as shown in the example below. One can choose a random string y of the length of that segment, and replace the segment by the random string y. The result is z. The macro mutational hill-climber did not need to use a population.

Nevertheless, several hypotheses have been put forward to explain results obtained by GAs. An adaptive mutation hypothesis is that where crossover in a GA serves as a mutation mechanism that is automatically adapted to the stage of convergence of the population. Crossover produces new individuals in the same region as the current population. Thus, crossover can be considered as an adaptive mutation method that reduces the strength of mutation as the run progresses.

Unlike the above hypothesis explanation of how a GA works, this explanation does make use of a population, but not through the building block hypothesis. If this is the more correct explanation of why a GA works on some problem, then this 3.

Thus, it would seem to suggest the use of a fairly disruptive crossover such as uniform crossover along with a strong selection method, such as a steady-state GA with both selection and worst-element deletion. There are two GA versions that more or less follow this outline. This algorithm does not do conventional crossover.

Instead, it does something called gene pool recombination, which is a form of a multi-parent recombination. Given a population, it first does a selection method on that population. It computes the order-1 schema proportions for the population after selection. Then it selects individuals for the next generation population using only those schema averages.

Each bit of each individual for the next generation is selected independently using the schema proportions as probabilities. They must add to 2. Professor in Polytechnic University, in was to show how genetic algorithms implemented in Department of Mathematics. He has 25 years teaching experience MATLAB with cluster analysis can be used to find the in algebra and applied mathematics. His research areas of interest minimum distance between two coordinates.

We used are applied mathematics and machine learning. FCA-merge Abstract [9] utilizes the idea that similar concepts classify Ontology mapping is the key challenge in the construction of documents will get similar result and concept lattice is semantic web, and a hinder in the handling of conflicts among used. And nearly all the above methods use hybrid heterogeneous data.

We propose an efficient ontology mapping matching strategy, especially Cupid [10], to enhance method which adopts a dichotomy approach. In this method, we mapping precision. Compared with the ontology mapping, every method has its merits, but no one method which doesnt use dichotomy approach, at the best case can solve the problem perfectly.

Experiments show that the method is proposed. In the conclusion of this paper, a accuracy of ontology mapping is also improved when the simplified compare study for these methods is presented. In this paper, our method holds the semantic net view that Keywords: Ontology Mapping, Semantic Web, Dichotomy meaning is inseparable from structure. Influenced by this Approach, Concept Similarity.

Introduction In the last few years, a lot of effort has been put in the 2. Ontology Mapping Process development of techniques that aim at the Semantic Web. A lot of those newly developed techniques requires and There are three steps in the process of ontology mapping, enables the specification of ontologies [1] on the web.

Reuse of existing ontologies is often not In the first step of ontology mapping, there are still two possible without considerable effort [2]. The first one is how the divide one When one wants to reuse different ontologies together, ontology into smaller ones, which is constrained by the those ontologies have to be combined in some way.

This structure of the ontology, and the other one is how to can be done by integrating the ontologies, or the compute concept similarity. The main frame of this step in ontologies can be kept separately. In both cases, the shown in fig. Ontology mapping, one that has received relatively little attention, becomes the key challenge in building semantic web.

Although there is already a lot of research done in this area, there are still many open questions [5]. Currently, the major method to carry out ontology mapping is based on heuristic or matching learning technique. Core concepts pick-up is the first step in ontology mapping.

This step aims at find the most general concepts Algorithm 1: core concepts selection in ontology, from which others concepts evolve or derive Input: adjacent matrix from. In order to do this, the structure of ontology must be Output: one or two core concepts Compute the degree of each node; surveyed. Set up an empty queue; WordNet[11] can be viewed as two light weight ontology. In our method, hubs are mapping decide whether node i is a core concept. ADJ i represents is first, which are most likely to corresponding to each other.

Algorithm 1: core concepts selection and is the premise for the other concepts to derive and evolve from. If we use graph structure to present ontology, marginal This step is aims at reduce the scale of ontology, so as to concepts are the concepts that locate at the verge of the reduce mapping complexity and improve mapping graph, which have the least number of neighbors and accuracy. In the process of ontology process, concept similarity In this paper, ontology uses graph structure. The graph will be calculated.

Then G can be depicted by an nn matrix concept similarity is different. The computing model also has two parts: Defiition1: the concept which has the largest degree First part is using recursive algorithm to compare locally is a Core Concept; on the contrary, the concept concepts attribute similarity; the second part is to which has the relatively smallest degree is a Marginal calculate concept similarity by using the amount of same Concept.

If not, it is a core concept; if yes, the more general concept node which it links by is-a relationship, is a core concept. Core concepts selection algorithm is shown in Fig2. After running algorithm 1, we will get several core Copyright c International Journal of Computer Science Issues. We need For every property p in p. There are three kinds of naming difference.

One if restriction is p d1 is due to use of abbreviations, acronyms, punctuation, etc Then if there exist p d1 in q. The other is due to languages exuberance. These if restriction is p x ways are not novel enough, so we dont discuss it in detail. In this step, the results of sub-ontologies if c disjoint d mapping is merged to generate the final result. The most then return 0; important thing is the merging order.

This kind of ontology is hard to find online, we establish them by ourselves. The worst case of ontology partition First, we collect reports about traffic accident online and randomly select 30 of them every time to establish At the best case, every time after ontology partition, ontology. We do 6 times to establish 6 ontologies for ontology will be divided into two sections which contain experiment. Ontology is semi-automatically established. At this time, pronoun, and adverb, only left noun and verb.

We chose one ontology as reference ontology. In this ontology, there are 29 concepts, namely traffic accident, loss, death, injured, salvage, ambulance, 4. Accuracy Estimation overspend , overload , medical staff , Police , insurance company and so on. TABLE 2: ontology mapping in field of traffic accident 4.

These ontology 1 32 59 89 75 ontologies are about book or magazines, and have 2 30 57 90 78 taxonomy structure, which resembles a tree. To study carefully, ontology which is about There are five kind of relationship between concepts, the field of animal classification, administration, namely compose of, is-a, caused-by, followed by university, document classification and so on has tree and is-done-by.

When using divide and conquer strategy, every Because of the complex relationship, ontology has a graph branch is a sub-ontology naturally, and doesnt needs to be structure other than a tree. For There are hubs in it, for example traffic accident loss salvage. Then, we kind, this is also the reason we dont give comparison. To contemplate on the reason, we find conquer strategy mapping accuracy is improved in these fields, core concepts exits explicitly and are Comparing our method with the other methods, it has easy to find, and the boundary of sub-ontology is clear.

So no advantage on such certain taxonomically organized the ontology is easy to partition and can get better result. But in feature. To contemplate on the reason, we find in these Acknowledgments fields, core concepts exits explicitly and are easy to find, This paper is supported by National Science Foundation of and the boundary of sub-ontology is clear.

So the ontology China and Innovative Foundation for is easy to partition and can get better result. To be more Graduates of Shanghai University A. Foundation for Doctor B Conclusions References [1] Thomsa R. Besides this, ontology 2, ,pp. Ontology Metadata for Ontology as identical concept discovery, ontology database Reuse, International Journal of Metadata, Semantics and maintenance, people information retrieval and so on. Ontologies, Vol. In this paper, we put forward divide and conquer, include [3] T.

Berners-Lee, J. Hendler, O. Lassila, The Semantic Web, core concepts pick-up and ontology partition. Besides, we Scientific American, Uschold, Where are the Semantics in the Semantic Web? Experiments and analysis both Same as Schema Evolution, Knowledge and Information show our method is reasonable and has its advantages in Systems, Vol.

Ontologies on the Semantic Web. So, our method should ranks on Hidden Markov Model. Journal of Southeast University first. References [] dont adopt this strategy; on the English Edition , Vol. Constructing Virtual sibling and rules all together, and adopts self-learning Documents for Ontology Matching.

FCA-merge [9] depends ,pp. The competence [11] Fellbaum, C. Accuracy: Comparing our method with the other methods, [12] Mark. Steyvers, Joshua B. Zhi Hui-Lai received Dr. Currently, he is a lecture in Henan Polytechnic University, China.

He has published 20 papers in the field of ontology engineering and formal concept analysis. His current research field is knowledge representation and extended model of concept lattice. Huo Zhan-Qiang received the Dr. His research interests are performance evaluation for wireless network system and vacation queuing systems.

His research interests are data analysis and disease prevention. Regarding the nature of the tracs and Machine-to-Machine M2M involves communication through a the cause of congestion, we can mainly distinguish two wire or wireless network without human intervention.

M2M on classes: Congestion in the user plane and in the control Cellular network, also defined as Machine Type plane. Communications MTC by 3rd Generation Partnership Project 3GPP , has shown great potential in the industry for its long Congestion in the user plane is caused by the amount of distance wireless advantage. Different from the traditional human data sent and received by devices. Although it happens to human H2H communications, M2M communications rarely since devices send and receive small amounts of involve a large number of terminals and network congestion may data, it may frequently happen that a lot devices send their occur due to simultaneous signaling or data messages from MTC data at the same time leading to a congestion mainly in the devices.

This paper describes the general anti-congestion solutions 1. The related work is presented in section Machine to Machine M2M communication is seen as a 2. The main solutions from 3GPP are analyzed in section3. Simulation results necessarily need human interaction. M2M are shown in Section 5 and further study is discussed in communications indicated the potential for machine-type Section 6.

However, wireless sensor networks or ad-hoc type networks in combination with fixed network communications are also a contender 2. Related work for the implementation of such applications. By exploitation section 3. However, the group is made by, not the cellular network itself, but No. In [4][5], MTC devices are grouped into control by the clusters based on QoS characteristics and requirements.

In [6], scheduling triggering of time- scheme is used to delay-tolerance MTC traffic at the controlled MTC operations expense of the handover bandwidth reserved resource. More details of these solutions are given in [10]. In the next section, we will propose a new anti- mobility UEs. The number of 4. The realization of Uplink UL MIMO is where Y is a N r 1 vector representing the received limited by the practical issue of the implementation of multiple power amplifiers in the UE, especially in the signals S is a N u 1 vector representing the hand-set.

The UL user throughput is also limited by the transmitted signals, H is a N r Nu channel matrix, n0 channel bandwidth. Orthogonality defect is an effective parameter to evaluate the orthogonality of the basis in a matrix [15]. Although to the low SNR user, the gap is not orthogonal to each other[16]. So the pairing criteria of so big. The simulations MTC server. The small scale fading is modeled as Downlink DL is relatively low e. Industrial metering Rayleigh at fading. Assume that all the UEs are fixed, and Surveillance systems.

It is totally different to which is very typical in MTC scenario. It is very UE traffic Full Buffer difficult to pair two nomadic cellphone users. Snapshot number C. MTC Machine Communications. China some MTC devices. Communications, ,8 1 Computer Network. Vehicular Technology algorithm is pre-defined and rarely re-configured. Double Communication, and we propose it to MTC scenario. Schubert, M. University of Science and Technology, Wuhan, China.

His current research interests Communications[S]. Opportunities for implementation machine-to-machine services via 3G mobile networks. He was a postdoctoral fellow with School of Computer Science and Engineering, Hongkong University, from to His Machine-type-communication device grouping algorithm for current research interests include cloud-computing , software congestion avoidance of MTC oriented LTE network.

Communications in Computer and Information Science, , In ,he joined Shenzhen Institute of 49 4 Information technology, China, as an associate professor. His current research interests include pattern recognition, software Copyright c International Journal of Computer Science Issues.

Li Huazhong, born in ,. He is an associate professor in Shenzhen Institute of Information Technology,. Xianyi Ren received the B. S and PH. She completed the post doctoral research in Research Institute of Tsinghua University in Shenzhen, in Her research interests include image processing and video surveillance and machine vision.

This paper proposes an tree structure in memory. And usually the TEXT part of the improved method of DOM parsing approach parsing node accounted for a lot of memory size in the XML approach of delaying extension and reducing redundancy. Therefore we should study the TEXT part of the This method reduces the size of the object created by node in order to reduce the memory size. The approach delaying expanded document, whose purpose is to reduce taken in this paper is the TEXT part of the node is not the memory size used.

At the same time it improves the expanded in the tree structure in memory, instead some performance of the system by reducing the redundancy of index. These indexes are the index number of the TEXT the string stored.

After analysing the new algorithm, part of the node in an array. At the same time we use the improvement on it by Hash table is used. It reduces process method of reducing data redundancy, which use the same time and increases parsing efficiency of system further. This index when the contents of the node are the same.

Seven different sizes of XML document were tested based on the new 2. The test results demonstrate this algorithm is feasible and effective. Introduction parsing approach. In the XML document. It not only provides a complete the dynamic array the same TEXT value only occupies one representation for XML document stored in the memory, memory space.

The same TEXT value only use an index but also provides the method to access to the entire number in the document tree expanded, which reduce the document randomly. The user can regard document as a data redundancy and the memory size. However, the consumption of memory size is very impressive when the document is very large.

In order to reduce the memory size, China we must improve existing DOM parsing approach. If not, go to step arule: rule1 arule rule3 2. They are used in this paper in order to it easier ; for realization of parsing algorithm. Add '' end form1. Add arry[strtoint trim a. In parsing process, when we read an element value, Procedure 5: modification algorithm first in dynamic array to find out whether them have the Input a,s,s1 : a is pointer of pointing at root node of same value.

If so, we only add index value found in the document tree generated after parse. If not, we assign a space for dynamic array, to be deleted. Then the corresponding index Output: Alter element contents to element node of document number is added in the document tree. When the XML tree specified in memory. At procedure modi a:point;s:string ; this time we need to compare with all dynamic array value begin to find out whether one of them has the same value as the if aNIL then element value being read.

If the key word of record is the same as K in 5 Serializable output algorithm of XML document structure, the store location must be location of f K. Procedure 6: Serializable output Consequently, we can get the record queried directly Input a : a is pointer of pointing at root node of document without comparison.

Output: Export document tree in memory as XML document. We call the corresponding relationship f for Hash function, procedure abc a:point ; and the table created according to the idea for Hash table. For XML document, the writeln text1,arry[strtoint trim a. But the 2 SJ SJ i ; 3 i 1 comparison of the average search length can be limited within limits. And direct map have been build between most of key words and store location of record. So Hash table can reduce comparand of search in parsing XML document and reduce the cost of time.

Its time complexity is O 1 , which is far less than In general, the conflict only reduces as much as possible. So how to deal with conflict redundancy by dynamic array , whose time complexity is is essential to structure hash table.

We use the "linear detection to hash method in open address method in order to resolve conflict. The reference 4. In Among them, H key is Hash function. As is shown in fig. When Computing, Vol. Computing, Vol. Conclusion 11, , pp. It can resolve the question of consumption of a lot of memory size and reduce redundancy Xiaoxia Sun born in She received her M.

University of China in , she is working towards a Ph. She is a lecturer 2 After analysing the new algorithm, improvement on it by at Taiyuan University of Science and Technology. Her main research Hash table is used. It reduces process time and increases interests include computer algorithm and conveying machinery. Hui Zhao born in He received his M.

Currently, he is a professor at Taiyuan University of Science and Technology. His main research interests include Electromechanical integration and Continuous conveying machinery. Zhang and R. The technique used here is to combine the anisotropic However, the disadvantage of the PM model is tending to diffusion PM model and total variation TV model. The new impair textures and details of image so that de-noising is technique utilizes both advantages of PM model and TV model, not sufficient in the whole process.

Another traditional while avoiding the disadvantages of both of them. To evaluate approach to partial differential equation based image our algorithm several experiments have been conducted. The processing techniques was proposed by Rudin, Osher, and experimental results affirm the high performance of our model. Fatemi ROF [5]. Total variation TV minimization is a successful approach to recover images 1.

Introduction with sharp edges. Nevertheless, TV model can cause Gibbs-type artifacts. These Gibbs-type artifacts cannot be Since the noise is related to high frequencies, it is difficult acceptable for applications like image feature, object to remove the noise, while preserving the important detection. In order to reduce the Gibbs-type artifacts 2 features [7].

The most efficient algorithm is the one that has the ability to solve this problem. Image de-noising has produced by PM, and TV models, we can use u as a measure of image smoothness. This can reduce the Gibbs- many applications that push people to look for better type artifacts, but unfortunately penalizes too much the algorithms to overcome the drawbacks of the existing gradients corresponding to edges [9]. In order to ones.

There are many algorithms of image de-noising, for simultaneously reduce the Gibbs-type artifacts without instance, multi-resolution geometry analysis, which is causing any damage in the image, we use a weighted based on wavelet theory [15]-[17], has attracted a lot of function combining the PM model and TV model to get attention. Recently, partial differential equation PDE our new model.

In Perona and Malik PM [4] variation models. The proposed method is described in proposed the anisotropic diffusion model, which is useful Section 3. In Section 4, we present our experimental tool for multi-scale description of images, image results that confirm the efficiency of proposed model. Some concluding remarks are presented in Section 5. The basic idea of PM is to evolve a family of smoothed image u x, y from the initial image u0 x, y , using the following partial differential equation: 2.

When the noise intensity is large, the gradient of the noise and the gradient of the 1 edge are similar, so the equation 11 cannot be used for g s. This is the reason why the PM model causes 1 Gibbs-type artifacts. This idea was introduced in by Tikhonov and u x, y, t u0 x, y. Here the time acts as a scale Arsenin [14].

The authors proposed to consider the parameter for filtering. Typically, g s is a non-negative following minimization problem: decreasing function, such that g s tends to zero as s 2 12 E u u u u 0 2 d xd y , approaches infinity.

One of the serious problems in the diffusion model is that it is very sensitive to noise. To where the first term of 12 is the smoothing term. The obtain reconstruction u of a degraded image u0 , Lp norm with p 2 of the gradient allows us to remove Nordstrm et al.

Rudin, Osher and Fatemi 2 E u u0 2 g u u k2 g u ln g u dxdy , 6 1 ROF [5] in proposed to use the L norm of the 2 gradient of u , instead of the L norm, that is, where u u , u is gradient operator, is the minimizing the total variations: x y domain of the image and is the Lagrange multiplier. But the disadvantage of TV model is that the block effect is produced when dealing with the flat areas, and consequently the local details u x, y dxdy u x, y dxdy , 0 14 characteristics of the original image are lost [10,11].

The weighted method proposed in this paper, which is 1 u x, y u0 x, y dxdy 2. The new 2 known variance. For the images of total variation, let us consider the Recently the authors in [6] multiplied TV model, and PM energy functional of the image as follows: 1 E u u dxdy. New Model The partial derivatives of the integrand F x, y , u , u x , u y u x2 u y2 18 In image processing, removal of noise without blurring the image edges is a difficult problem.

Typically noise is are: characterized by high spatial frequencies in an image, and ux uy the details of the image, such as edge and texture, Fu 0 , Fu x 1 , and Fu y 1. The task 2 u u 2 2 2 u u 2 2 of image filtering is to remove the noise and preserve the x y x y details simultaneously, namely, to have possibly least The corresponding Euler-Lagrange equation to 16 is: diffusion in the regions which contain more image features, and possibly most diffusion in the regions which contain less image features.

In this paper we use a ux uy u 0Fu Fux Fuy , 1 , 1. Considering the ux uy ux uy characteristics of the anisotropic diffusion de-noising By introducing Lagrange multiplier , the energy model and the total variation model, we use a weighted functional of the image can be redefined as: function combining the two models to get a new de- E u u u u0 2 dxdy 20 noising model, which provides a new approach for solving the contradiction in the image restoration.

Now we restore the original image u by the degraded image To obtain the minimum, the energy functional needs to satisfy: u0 , taking the energy functional of the image as follows: u E u u 1 k2 ln 1 u u0 2 dxdy, 23 2 u u0 0 21 g u u where the weight function 0,1. By the gradient descent method, we get the TV de-noising From 10 , 21 the corresponding Euler-Lagrange model as follows: equation is: u 1 g u u 2 u u0 0. To solve problem 25 by using the finite difference method, we let 4.

Experimental Results and Analysis u u 2u 2u x u y u xy u y2 u xx , 27 T x yy u 2 2 2 3 In this section we compare the proposed approach with u x u y other methods in terms of the visual quality of de-noising P g u u images, Mean Square Error MSE according to 36 , and 28 k 2 u x x k 2 u y y u x2 u xx u 2y u xx 4 u x u y u x y u x2 u yy u y2 u y y Peak Signal to Noise Ratio PSNR according to Replacing PSNRdB 10 lg 37 MSE the first order derivatives by central divided differences and the second order derivatives by forward divided Where N x and N y are the number of pixels horizontally differences, we can rewrite the new model as the discrete form as follows: and vertically respectively, and I de i, j , I or i, j are the de-noised image and original image, respectively.

T n 1 P n 2 u n u0 , 29 u We take the commonly used bit standard where, n 0,1,2, Lena, Cameraman and Boat images processed by the Introduce the space discrete sign: different de-noising methods as examples. The experimental results are shown in Figures 1 and 2. In n 1 n these two cases we select the parameters as follows: u n 1 u n ui , j ui , j , the time step size t 0. In Figure 2 the image is TV 9. Figures 1 and 2 show that the new Ref.

The quantitative results are presented in Tables 1, 2, 3. It can be seen from Tables 1 and 2 that the PSNR of the new model is the maximum, and from Table 3 we can see that the MSE of our model is the minimum, that means the de- noising effect of the proposed model is the best. From Table 2 we find that increasing the variance of the noise will decrease the PSNRs of the four algorithms, which means that the de-noising effect is worse.

However the 1 2 PSNR of the new model is the largest among the four algorithms for the same variance, which means the de- noising effect of the new algorithm, is the best in terms of PSNR. From Table 3 we find that MSEs of the four models increase as the variance of the noise increases, which means that the de-nosing effect is worse.

Nevertheless the MSE of the proposed model is the lowest among the four models for same variance, this means the de-noising effect of the new model, is the best in terms of 3 4 MSE. From algorithm, 3 Result of reference [6] algorithm, 4 Result of the new algorithm.

Figure 4 we can see that the proposed method in all noise power has the highest PSNR. From Figure 5 we can see that the proposed method in all noise power has the lowest MSE. Variance 0. Conclusions In this paper we propose a new approach for image de- noising based on the combination of PM model and TV 0 0 model. In our model we reduced the noise by optimizing 0 50 0 50 the energy functional. From the performance of the 1 2 simulations, our model has more de-noising ability in terms of MSE, PSNR, and visual quality compared with the anisotropic diffusion PM model, the total variation TV model and reference [6] model.

To evaluate the proposed algorithm, several experiments were presented. The proposed algorithm can also be extended Figure 3: Histograms of Fig. If other methods for solving the 2 Histogram of image 2 , 3 Histogram of image 3 , partial differential equations are applied to new model, and 4 Histogram of image 4. In future research we will focus on constructing better algorithms 29 in smoothing images and preserving image features.

Li, F. Gao, and N. Cai, A new algorithm for removing 19 0. Nordstrom, Biased anisotropic diffusion - a unified regularization and diffusion approach to edge detection, Image Vision Computing, Vol. Teubner Stuttgart, New [4] P. Perona, and J. Rudin, S. Osher, and E. Zhange, R.

Wang, and L. Jiao, Partial differential equation model method based on image feature for 12 denoising. Chatterjee, and P. Milanfar, Is denoising dead? You, W. Xu, A. Tannenbaum, and M. Aubert, and P. Lysaker, A. Lundervold, and X. Tai, Noise removal using fourth-order partial differential equation with application to medical magnetic resonance images in space and time, IEEE Transactions on Image Processing, Vol. Lysaker, S. Osher, and X. Ling-hua, and Y. Hong-wei, Combined model for image denoising based on partial differential equations Computer Engineering and Applications, Vol.

Tikhonov, and V. Kalavathy, and R. Suresh, Analysis of image denoising using wavelet coefficient and adaptive subband thresholding technique, International Journal of Computer Science Issues, Vol. Adeli, F. Tajeripoor, M. Zomorodian, and M. Neshat, Comparison of the fuzzy-based wavelet shrinkage image denoising techniques, International Journal of Computer Science Issues, Vol. Volar, and A. Karthik, V. Hemanth, K. Soman, V. Balaji, Sachin Kumar S, and M. Ali Abdullah Yahya received his Ms.

Jieqing Tan received his Ph. His research interests include nonlinear numerical approximation, scientific computing, computer aided geometric design, computer graphics and digital image processing. So far, the research of cropped algorithm, to disperse foundation pit. And on this basis we use Flex technology to design a vector graphics-based three- foundation pit excavation is concentrated in the calculation dimensional simulation method of foundation pit construction.

But we need a software especially you can easily edit the construction progress and quickly web-based applications that directly reflect the progress of simulate the excavation of foundation pit. Keywords: Vector Graphic, flex, foundation pit, simulation, 3D model. Foundation pit excavation is very common in construction, especially in underground engineering. It's a very significant thing understanding real-time of the foundation 1. Introduction pit construction progress. We designed a foundation pit excavation simulation method based on network and vector Information technology is increasingly used in civil graphics.

It can effectively solve the problem of update and engineering constructions. We can manage engineering simulation in foundation pit excavation. Most project work sites are distributed in various different locations, so the 2 Foundation pit's basic property edit management is very difficult.

For managers, they of course want to be able to see the situation of every work site more For a foundation pit, its outer contour in the plane can be conveniently and fast. Nowadays, connecting to the abstracted into a polygon. Therefore, when a foundation Internet at work sites is more convenient in 3G and WIFI pit has been established, we can click the mouse on the network coverage. Therefore ,we may consider to establish corresponding position of the map to generate the vertices a Web-based application that the construction workers at of the polygon.

Then we connect these vertices to describe work site can easily upload and update construction the edge of foundation pit that being excavating. The progress and managers can view it remotely via computer points' connection order is based on the order in which simulation technology.

Finally the polygon which represent the foundation Many scholars and scientists have done research for pit's contour is obtained. There are several notices: 1. The construction simulation. Wu, Borrmann [1] presents a intersection points of segments which connecting the method in automating the generation of time schedules for adjacent vertices should all be coincide with vertices, or bridge construction projects.

Scherer and Schapke [2] the foundation pit's contour is not a polygon. As is shown developed a management information system to support in fig. Lmmer, foundation contour points which depicted by user. A is a Meiner [3] presented a research for the integration of polygon but B isn't, because B has excess intersection.

The clicks must First calculate the intersection points of two polygon, then be on the order in accordance with the anti-clockwise. In sort the vertices of each polygon and its intersection points. Accordingly, in order to facilitate the calculation of the next step, the selection clicks must be strictly in When a grid located entirely in a foundation pit, it means accordance with the counterclockwise direction sequence the intersection of the grid and the foundation pit is the along the foundation pit's contour.

As shown in C, grid itself. Therefore, to calculate the square grid and although there is no excess intersection, the points' order is foundation pit polygon intersection, we can get the discrete counterclockwise, the area is negative. Therefore, it doesn't plane polygon mesh of foundation pit. Due to different meet the requirements of a polygon. Thus, in Fig. Therefore, we should pit. For each foundation pit, its contour data would only be allow user to customize the grid cell direction, to get a entered once in the establishment of base data.

In addition, both the sizes of foundation pits and the accuracy requirements of simulation are different, so the side length of the grid is also needed to be defined by user. When the grid mesh is constructed, user can click on a grid or select all grids in a region, to set the Fig. But it must be changed into discrete objects when storing into computer. In order to facilitate editing and finding, we have adopted a regular grid expression which is common used in geographic Fig.

A foundation pit is divided into array of grid cells, most cells are square and each has a different elevation attribute. We use these grids to simulate the 3 Simulation of foundation pit foundation pit entity. The contour of a foundation pit is an irregular polygon, therefore, its coverage is scarcely 3. So we So far, we have proposed a method using regular grids to need to calculate the intersection of the boundary of grid disperse and assign a foundation pit.

But each grid needs and foundation pit contour, to clip the parts which located user to set the elevation manually, and with the project outside the foundation pit and belongs to the grids. In other progress it will constantly change. To reduce the workload words, we take the intersection of two polygons. There are in meshing, user usually will not choose too small division. The But the problem is, when the three-dimensional simulation results of cross-intersection may be one or more polygons, displays, if directly use this mesh for modeling, it will be as shown in Fig.

The solution is to use two different mesh density: one for edit and the other for display. The mesh grids for editing are larger than the displaying ones. When displaying, the editing mesh is automatically refined by elevation interpolation to create a mesh with smaller grids.

This seems to be rather smooth. Elevation interpolation using the common terrain Fig. The construction 3D solid in computer is actually expressed by triangle progress editing module is when foundation pit entity is mesh surface.

Therefore, the 3D solid model to simulate already established in system, we allow users to edit and the excavation of foundation pit, is to discrete the update the construction schedule data according to the excavation surface into triangular mesh in space, actual construction condition. The view interface is essentially. We have adopted interpolation to increase available to both constructors and project managers to use, density for initial mesh created by user in 3.

Next, we'll it focuses on exhibition of foundation pit's excavating use these mesh vertices to generate triangular mesh with condition via 3D technology. Overall, the purpose of boundary constraints, the boundary is the polygon outline editing interface is allow users to easily set up the basic of foundation pit. The steps are: 1. According to the density data and the properties such as elevation of each part in of grid cells, add more points along each side of the foundation pit, and for view interface is to directly reflects contour polygon, to meet the empty circle characteristic of the excavation progress to users.

The system structure Delaunay mesh; 2. Build Delaunay triangulation based on distribution is shown in Fig. The triangular mesh is a data structure of B-Rep expression. It can be passed directly to 3D engine for rendering and display. The comparison of plane mesh and 3D modeling effect is show in Fig. We can also obtain any cross-section of the foundation pit, as shown in Fig. In technical level, because of Flex has outstanding display Fig. The front-end interface using Flex for development and background data is processing by Java.

Constructors edit data in the editing interface, than the interface can interact with Java program through AMF protocol or WebService to pass foundation pit data to the Java side, Java program update the corresponding database table via JDBC driver.

View interface is also a Flex application, it interacts with Fig. In flash Player In the front-end we imply 3D simulation based on vector graphics. In background we do business logic and 3D space algorithms. The front-end part 5 Conclusions needs to provide two different interfaces for editing and Use this method and Flex technology, we developed a view.

According to the vector animation features of Flex, we foundation pit, the other for editing construction progress designed a method to simulate the excavation depths of of foundation pit. The basic data editing module is that foundation pit based on vector graphics, it is easy Copyright c International Journal of Computer Science Issues. In actual engineering apply, the software can be combined with monitoring point data, foundation pit excavation envelope, shoring of trench and various other data.

The system can be further improved and display project progress all round in a higher degree. Grant No. Wu, I. Advanced Engineering Informatics, Scherer, R. Schapke, A distributed multi-model- based Management Information System for simulation and decision-making on construction projects.

Lmmer, L. Meiner, and M. Petersen, Object-oriented integration of construction and simulation models. Ruwanpura, J. Ariaratnam, Simulation modeling techniques for underground infrastructure construction processes. Tunnelling and Underground Space Technology, Luo, Z.

Zhang, and Y. Journal of Hydrodynamics, Ser. B, Zhou, N. Engineering Geology, Song, L. His research interests includes Web3D technology, stratigraphic modeling and computer application in underground engineering. He is good at computer application and data processing.

Santhi1, S. Maria Wenisch2 and Dr. Abstract The spam mails are used by spammers to steal the data of the Countries Percentage users and organizations online. Rapid growth rate of the use of the internet has increased the spam mails. There are several U. This approach is to classify China 8. This work classifies the emails using word ranking database and the India 4.

For this purpose the work has considered only the content of the email. France 3. Italy 3. Introduction Russia 2. Spain 2. Unsolicited commercial mails are often sent by the spammer to illegally promote a service or Other Spam became an issue when the internet was Table 1: Spam Rate opened to the public. Internet users are forced to receive December to Feb. Spammers harvest the address Courtesy Sophos Lab of internet users from various sources and serious cause inconvenience to the users.

The users inbox are flooded Spammers collect the users personal bank details and also with enormous spam mails. A is leading spam hang the users systems by spreading virus. Spam mails relaying country with Every internet user should spend few of the world population by Spam mails are treated as illegitimate or black listed mails and ham as legitimate or white listed mails.

The targets of the spammers are to send bulk mails to the internet users in order to receive response from few with a view to secure profit for them. Spammers are paying money to collect the users explains the related work. Section 3 describes the proposed address. Spammers are used to send attachments with work. Section 4 discusses the expected result.

Finally virus. The images sent by the spammers have an Section 5 concludes the Paper. It tracks and gathers details about when and where each particular recipient reads email and IP address of the computer. It is 2. Related Works difficult for the user to detect the object embedded in the image. The text of the message is stored in the image and MD. Rafiqul Islam et al discussed about different saved as gif or jpeg image and displayed in the email. Their either by filtering the spam messages at inbound level or research includes a study of automated filtering and outbound level.

They the user inbox level to filter the spam. Like Naive presented a comparative analysis on different filtering Bayesian classification, Support Vector Machines for text techniques and its advantages. Spammers are increasingly employing spam mails from the Internet service providers in its heavy innovative methods to send their spam mails. Some traffic. Finger print method is used to detect the similar organizations and many researchers have tried to filter earlier mails and sets a parameter for the email category.

Spam filtering is necessary to protect the internet information. By simply adding the entry in the MD and users which is quite challenging. Spam filter is a program delete the unimportant mails. They explained about the or software used to filter spam mails. They are automatic hand-free deployment and online update mechanism, high accuracy There are several algorithms available for filtering spam. Christina et al proposed a study on email spam filtering Seongwook Youn et al proposed a comparative techniques.

They discussed about various problems study for email classification. Neural Network, SVM, aroused by spam, different filtering methods and Naive Bayesian and J48 classifiers are used to filter spam techniques are used to filter spam.

Hailong Hou et al from the datasets of emails. J48 is a decision tree creates a developed a method of hyperbolic tree based binary tree used for classification of legitimate and spam. Gregory L. They suggested that learning based techniques of spam filtering. This paper anti-spam developers should not only concentrate in discussed about the learning based methods of spam filtering of spam but also should consider the costs filtering like keyword filtering, image based filtering, associated with spam filtering.

In this work fuzzy logic is applied to classify spam. This They presented the evaluation and comparison of the work used a fuzzy inference system to classify spam words results obtained from the various filtering methods. Words are extracted from the content of the emails which, this work compares them against a list of Ali Cltk et al proposed a method of spam email spam words stored in the database ranked with its values filtering methods with high accuracies and low time and categorizes the words in accordance to the ranking.

They took Turkish mails for their research. Fuzzy inference system finally takes the input value from They used PC-KIMMO system, a morphological analyzer the above ranking and classifies the output as least to extract root forms of words as input and produce parse dangerous or moderate or most dangerous spam mail. This method is based on the n-gram approach and a heuristics.

They developed two models, a class general model and an e-mail specific model. The second model determines the correct system machines and k-nearest neighbor are used for spam class of a message by comparing it with the similar filtering. Recall and precision are the two ways for previous message for matching. The third model is a presenting the system performance. SVM is used as the combined perception refined model. It is a combination of best classifier for English and ME performed better than above two models.

Free word order is used for ordering NB in Arabic messages. They suggested increasing the the word in fixed order for n gram model. This spam parameter will improve the performance. They faced the increase of time mail in a proactive way which intercepts the complexity problem when handling the larger number of communication held between spambot and the intended words.

Adaboost ensemble algorithm is used to compare server and redirects its communication with local mail with its previous work. They performed extensive tests on server at the gateway. They collect spam messages at the various number datasets sizes and initial words. They have gateway and obtain the current spam messages sent by obtained a result of high success rates in both Turkish spambotnet. They clean the machine system using a language and English.

They have collected the spam messages by resetting the A. Lpez-Herrera et al developed a honeypot. The next process is filtering the message in a multiobjective evolutionary algorithm for filtering spam. Longest common string algorithm is used They evaluated the concepts of dominance and paretoset. Longer emails are and recall. PUI datasets are used for spam filtering. They took second email from the list legitimate emails and labeled as spam.

They used the weak and merge it with to form a second raw template named filtering rules high precision and low recall for labeling a which was more specific than previous one. Then they minimum portion of spam emails. If the removed text Liu Pei-yu et al suggested the method of improved percentage is below a predefined threshold then they are bayesian algorithm for filtering spam. KNN algorithm, treated as their new. The email used to form was SVM, decision tree, and improved bayesian algorithm are removed from the list and they continued with third step.

If used for classifying texts. KNN algorithm is a simple and the changed text percentage is above the current is too accurate method for spam filtering by using the k nearest generic and is therefore discarded.

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