Artificial Life and Robotics

, Volume 2, Issue 1, pp 48–52 | Cite as

A self-organizing genetic algorithm for multimodal function optimization

  • Il-Kwon Jeong
  • Ju-Jang Lee
Original Article


A genetic algorithm (GA) has control parameters that must be determined before execution. We propose a self-organizing genetic algorithm (SOGA) as a multimodal function optimizer which sets GA parameters such as population size, crossover probability, and mutation probability adaptively during the execution of a genetic algorithm. In SOGA, GA parameters change according to the fitnesses of individuals. SOGA and other approaches for adapting operator probabilities in GAs are discussed. The validity of the proposed algorithm is verified in simulation examples, including system identification.

Key words

Genetic algorithm Self-organizing 


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Copyright information

© ISAROB 1998

Authors and Affiliations

  1. 1.Department of Electrical EngineeringKorea Advanced Institute of Science and TechnologyTaejonKorea

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