The application of modified hierarchy genetic algorithm based on adaptive niches

  • Authors:

  • Wei-Min Qi;Qiao-ling Ji;Wei-You Cai

  • Affiliations:

  • College of Physics and Information engineering, Jianghan University, Wuhan, China;College of Power and Mechanical engineering, Wuhan University, Wuhan, China;College of Power and Mechanical engineering, Wuhan University, Wuhan, China

  • Venue:
  • ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
  • Year:
  • 2005

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Abstract

Traditional genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche hierarchy genetic algorithm (ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator to adjust the crossover rate and probability of mutation of each individual, whose mutation values are decided using individual gradient. This approach is applied in Percy and Shubert function optimization. Comparisons of niche genetic algorithm (NGA), hierarchy genetic algorithm (HGA) and ANHGA have been done by establishing a simulation model and the results of mathematical model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum.