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Fuzzy Adaptive Search Method for Parallel Genetic Algorithm Tuned by Evolution Degree Based on Diversity Measure

  • Yoichiro Maeda
  • Qiang Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4529)

Abstract

Generally, as for Genetic Algorithms (GAs), it is not always optimal search efficiency, because genetic parameters (crossover rate, mutation rate and so on) are fixed. For this problem, 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. On the other hand, in order to improve the solution quality of GA, Parallel Genetic Algorithm (PGA) based on the local evolution in plural sub-populations (islands) and the migration of individuals between islands has been researched.

In this research, Fuzzy Adaptive Search method for Parallel GA (FASPGA) combined FASGA with PGA is proposed. Moreover as the improvement method for FASPGA, Diversity Measure based Fuzzy Adaptive Search method for Parallel GA (DM-FASPGA) is also proposed. Computer simulation was carried out to confirm the efficiency of the proposed method and the simulation results are also reported in this paper.

Keywords

Diversity Measure Fuzzy Rule Migration Rate Crossover Rate Gray Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Yoichiro Maeda
    • 1
  • Qiang Li
    • 1
  1. 1.Dept. of Human and Artificial Intelligent Systems, Graduate School of Engineering, Univ. of Fukui, 3-9-1 Bunkyo, Fukui 910-8507Japan

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