Applied Intelligence

, Volume 15, Issue 2, pp 117–130 | Cite as

An Endosymbiotic Evolutionary Algorithm for Optimization

  • Jae Yun Kim
  • Yeongho Kim
  • Yeo Keun Kim


This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.

coevolutionary algorithm endosymbiosis optimization localized coevolution 


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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Jae Yun Kim
    • 1
  • Yeongho Kim
    • 2
  • Yeo Keun Kim
    • 1
  1. 1.Department of Industrial EngineeringChonnam National UniversityKwangjuRepublic of Korea
  2. 2.Department of Industrial EngineeringSeoul National UniversitySeoulRepublic of Korea

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