Title: A Novel Self-adaptive Chaotic Genetic Algorithm
Abstract: This paper presents a new real-value encoding self-adaptive chaotic genetic algorithm to solve optimization problem based on the analysis for shortcoming of standard binary-encoding genetic algorithm.It is used the entropy based on information theory to initialize population with better distribution and designed a crossover operator in light of probability distribution function and a self-adaptive chaotic mutation operator combined chaotic dynamic character with artificial neural network theory,which maintains population diversity to prevent and overcome premature phenomena in the evolutionary process.This algorithm is easy to implement with the simple operation.Several typical benchmark function numerical experiments demonstrate that it is improved on the solution precision and increased convergence speed.The proposed method provides an effective new method to solve the function optimization problems.
Publication Year: 2006
Publication Date: 2006-01-01
Language: en
Type: article
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Cited By Count: 7
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