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Review on Adaptive Genetic Algorithm and Metaheuristic Methods Within Stochastic Optimisation Int.J.Curr.Microbiol.App.Sci (2018) Special Issue-6: 431-441 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Special Issue-6 pp. 431-441 Journal homepage: http://www.ijcmas.com Review Article Review on Adaptive Genetic Algorithm and Metaheuristic Methods within Stochastic Optimisation Manjusha P . Dhoke, Jyoti S. Kale And Pavan K. Dhoke Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, Maharashtra, India *Corresponding author ABSTRACT In computer science and operations research, a genetic algorithm (GA) is a Ke yw or ds metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are Adaptive genetic commonly used to generate high-quality solutions to optimization and algorithm, search problems by relying on bio-inspired operators such as mutation, Stochastic crossover and selection. In a genetic algorithm, a population of candidate optimisation solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions. Introduction In genetic algorithm each candidate solution used in the next iteration of the algorithm. has a set of properties (its chromosomes or Commonly, the algorithm terminates when genotype) which can be mutated and altered; either a maximum number of generations traditionally, solutions are represented in has been produced, or a satisfactory fitness binary as strings of 0s and 1s, but other level has been reached for the population. encodings are also possible.[2] The evolution usually starts from a population of randomly A typical genetic algorithm requires: generated individuals, and is an iterative process, with the population in each iteration A genetic representation of the solution called a generation. In each generation, the domain, fitness of every individual in the population is evaluated; the fitness is usually the value A fitness function to evaluate the solution of the objective function in the optimization domain. problem being solved. The more fit individuals are stochastically selected from A standard representation of each candidate [2] the current population, and each individual's solution is as an array of bits. Arrays of genome is modified (recombined and other types and structures can be used in possibly randomly mutated) to form a new essentially the same way. The main property generation. The new generation of that makes these genetic representations candidate solutions is then convenient is that their parts are easily 431 Int.J.Curr.Microbiol.App.Sci (2018) Special Issue-6: 431-441 aligned due to their fixed size, which The fitness function is defined over the facilitates simple crossover operations. genetic representation and measures the Variable length representations may also be quality of the represented solution. The used, but crossover implementation is more fitness function is always problem complex in this case. Tree-like dependent. For instance, in the knapsack representations are explored in genetic problem one wants to maximize the total programming and graph-form value of objects that can be put in a representations are explored in evolutionary knapsack of some fixed capacity. A programming; a mix of both linear representation of a solution might be an chromosomes and trees is explored in gene array of bits, where each bit represents a expression programming. different object, and the value of the bit (0 or 1) represents whether or not the object is in Once the genetic representation and the the knapsack. Not every such representation fitness function are defined, a GA proceeds is valid, as the size of objects may exceed to initialize a population of solutions and the capacity of the knapsack. The fitness of then to improve it through repetitive the solution is the sum of values of all application of the mutation, crossover, objects in the knapsack if the representation inversion and selection operators. is valid, or 0 otherwise. Initialization In some problems, it is hard or even impossible to define the fitness expression; The population size depends on the nature of in these cases, a simulation may be used to the problem, but typically contains several determine the fitness function value of a hundreds or thousands of possible solutions. phenotype (e.g. computational fluid dynamics is used to determine the air Often, the initial population is generated resistance of a vehicle whose shape is randomly, allowing the entire range of encoded as the phenotype), or even possible solutions (the search space). interactive genetic algorithms are used. Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to Genetic operators be found. The next step is to generate a second Selection generation population of solutions from those selected through a combination of During each successive generation, a portion genetic operators: crossover (also called of the existing population is selected to recombination), and mutation. breed a new generation. Individual solutions are selected through a fitness-based process, For each new solution to be produced, a pair where fitter solutions (as measured by a of "parent" solutions is selected for breeding fitness function) are typically more likely to from the pool selected previously. By be selected. Certain selection methods rate producing a "child" solution using the above the fitness of each solution and methods of crossover and mutation, a new preferentially select the best solutions. Other solution is created which typically shares methods rate only a random sample of the many of the characteristics of its "parents". population, as the former process may be New parents are selected for each new child, very time-consuming. and the process continues until a new 432 Int.J.Curr.Microbiol.App.Sci (2018) Special Issue-6: 431-441 population of solutions of appropriate size is generated. Although reproduction methods In addition to the main operators above, that are based on the use of two parents are other heuristics may be employed to make more "biology inspired", some research[3][4] the calculation faster or more robust. The suggests that more than two "parents" speciation heuristic penalizes crossover generate higher quality chromosomes. between candidate solutions that are too similar; this encourages population diversity These processes ultimately result in the next and helps prevent premature convergence to generation population of chromosomes that a less optimal solution.[6][7] is different from the initial generation. Generally the average fitness will have Termination increased by this procedure for the population, since only the best organisms This generational process is repeated until a from the first generation are selected for termination condition has been reached. breeding, along with a small proportion of Common terminating conditions are: less fit solutions. These less fit solutions ensure genetic diversity within the genetic A solution is found that satisfies minimum pool of the parents and therefore ensure the criteria genetic diversity of the subsequent generation of children. Fixed number of generations reached Opinion is divided over the importance of Allocated budget (computation time/money) crossover versus mutation. There are many reached references in Fogel (2006) that support the importance of mutation-based search. The highest ranking solution's fitness is reaching or has reached a plateau such that Although crossover and mutation are known successive iterations no longer produce as the main genetic operators, it is possible better results to use other operators such as regrouping, colonization-extinction, or migration in Manual inspection genetic algorithms.[5] Combinations of the above It is worth tuning parameters such as the mutation probability, crossover probability The building block hypothesis and population size to find reasonable settings for the problem class being worked Genetic algorithms are simple to implement, on. A very small mutation rate may lead to but their behavior is difficult to understand. genetic drift (which is non-ergodic in In particular it is difficult to understand why nature). these algorithms frequently succeed at generating solutions of high fitness when A recombination rate that is too high may applied to practical problems. The building lead to premature convergence of the genetic block hypothesis (BBH) consists of: algorithm. A mutation rate that is too high may lead to loss of good solutions, unless A description of a heuristic that performs elitist selection is employed. adaptation by identifying and recombining Heuristics "building blocks", i.e. low order, low 433 Int.J.Curr.Microbiol.App.Sci (2018) Special Issue-6: 431-441 defining-length schemata with above there is a reasonable amount of work that average fitness. attempts to understand its limitations from the perspective of estimation of distribution A hypothesis that a genetic algorithm algorithms.[11][12][13] performs adaptation by implicitly and efficiently implementing this heuristic. Limitations Goldberg describes the heuristic as follows: There are limitations of the use of a genetic algorithm compared to alternative "Short, low order, and highly fit schemata optimization algorithms: are sampled, recombined [crossed over], and resampled to form strings of potentially Repeated fitness function evaluation for higher fitness. In a way, by working with complex problems is often the most these particular schemata [the
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