Species co-evolutionary algorithm: a novel evolutionary algorithm based on the ecology and environments for optimization

Wuzhao Li, Lei Wang, Xingjuan Cai, Junjie Hu, Weian Guo

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

In classic evolutionary algorithms (EAs), solutions communicate each other in a very simple way so the recombination operator design is simple, which is easy in algorithms’ implementation. However, it is not in accord with nature world. In nature, the species have various kinds of relationships and affect each other in many ways. The relationships include competition, predation, parasitism, mutualism and pythogenesis. In this paper, we consider the five relationships between solutions to propose a co-evolutionary algorithm termed species co-evolutionary algorithm (SCEA). In SCEA, five operators are designed to recombine individuals in population. A set including several classical benchmarks are used to test the proposed algorithm. We also employ several other classical EAs in comparisons. The comparison results show that SCEA exhibits an excellent performance to show a huge potential of SCEA in optimization.
Original languageEnglish
JournalNeural Computing and Applications
Number of pages10
ISSN0941-0643
DOIs
Publication statusPublished - 2015

Keywords

  • Artificial Intelligence
  • Software
  • Evolutionary algorithm
  • Optimization
  • Recombination operator
  • Species co-evolution algorithm
  • Algorithms
  • Co-evolution
  • Co-evolutionary algorithm
  • Comparison result
  • Evolutionary algorithms (EAs)
  • Recombination operators
  • Evolutionary algorithms

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