Fuzzy Adaptive Search Method for Parallel Genetic Algorithm Based on Evolution History

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概要

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• Genetic Algorithms (GAs) are well known as evolutionary computation with techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. But GAs have several problems that is the premature convergence and falling in the local solution. For these problems, we have already proposed Fuzzy Adaptive Search Method for GA (FASGA) that is able to tune the genetic parameters according to the search stage by the fuzzy reasoning. And as the improve method of FASGA, we have also proposed Fuzzy Adaptive Search method for Parallel Genetic Algorithm (FASPGA) and proved that it could avoid premature convergence problem well. However, there are some cases when it is not enough accuracy to describe the stage of evolution, because only the best fitness and average fitness were adopted as inputs of fuzzy rules. Therefore, as an improved method for FASPGA, we propose Evolution History based on Fuzzy Adaptive Search method for Parallel GA (EH-FASPGA). In EH-FASPGA, the evolution degree of each island can be grasped by the evaluation of evolution history. Then genetic parameters are tuned according to the evolution degree of each island.

• バイオメディカル・ファジィ・システム学会の論文

著者

• Maeda Yoichiro

  Dept. Of Human And Artificial Intelligent Systems Graduate School Of Engin. University Of Fukui

• Li Qiang

  Dept. Of System Design Engin. Graduate School Of Engin. University Of Fukui

• LI Qiang

  Dept. of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui

関連論文

• Fuzzy Adaptive Search Method for Parallel GA Based on Diversity Measure(Contribution to 21 Century Intelligent Technologies and Bioinformatics)
• Fuzzy Adaptive Search Method for Parallel Genetic Algorithm Based on Evolution History

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