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Function optimization using evolutionary programming with self-adaptive cultural algorithms

  • ChanJin Chung
  • Robert G. Reynolds
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1285)

Abstract

Self-adaptation can take place at several levels in evolutionary computation system. Here we investigate relative performance of two different self-adaptive versions of Evolutionary Programming(EP). One at the individual level adaptation proposed by Schwefel and Saravanan & Fogel and one at the population level using Cultural Algorithms. The performance of the two versions of self-adaptive EP are then compared to each other for a set of selected unconstrained function optimization problems. For most optimization problems studied here, the pooling of information in the belief space at the population level improves the performance of EP.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • ChanJin Chung
    • 1
  • Robert G. Reynolds
    • 1
  1. 1.Department of Computer ScienceWayne State UniversityDetroitUSA

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