An Adaptive Genetic Algorithm Based on Population Diversity Strategy

Abstract

Genetic Algorithms are adaptive methods which may be used to solve search and optimization problems. Three basic operations in Genetic Algorithms are selection, crossover and mutation, an important problem using Genetic Algorithms is the premature convergence in local optimum. This paper presents an adaptive genetic algorithm which adjusts probability of mutation dynamically based on average square deviation of population fitness value that shows the population diversity to solve premature problem. Compared Analysis shows the proposed adaptive Genetic Algorithm is efficient to avoid premature.