Mathematical Study OF Adaptive Genetic Algorithm (AGA) with Mutation and Crossover probabilities
Keywords:
crossover probability (Pc ), mutation probability(Pm)Abstract
GAs is powerful search techniques that are used successfully to solve problems in many different disciplines.AGA are particularly easy to implement and promise substantial gains in performance. AGA has some parameters, such as population size, the crossover probability (Pc), the mutation probability (Pm) are varied while genetic algorithm is running. Genetic algorithm includes these parameters that should be adjusting so that the algorithm can provide positive results. The main aim of this paper is that how to design of adaptive crossover probability (Pc) and mutation probability (Pm).By varying Pc and Pm adaptively it response to the fitness values of the solution.
Depending on the fitness value of the solution, in AGA the crossover probability (Pc ) and the mutation probability(Pm) are varied. High- fitness solutions are „protected‟, while solutions with sub average fatnesses are totally disrupted, that is by varying the crossover probability (Pc ) and the mutation probability(Pm) adaptively in response to the fitness value of the solution: when the population tends to get struck at a local optimum, the crossover probability (Pc ) and the mutation probability(Pm) are increased and when the population is scattered in the solution space, the crossover probability (Pc ) and the mutation probability(Pm) are decreased.
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