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Function optimization using evolutionary programming with self-adaptive cultural algorithms

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Simulated Evolution and Learning (SEAL 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1285))

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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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Authors and Affiliations

  1. Department of Computer Science, Wayne State University, 48202, Detroit, MI, USA

    ChanJin Chung & Robert G. Reynolds

Authors
  1. ChanJin Chung
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  2. Robert G. Reynolds
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Xin Yao Jong-Hwan Kim Takeshi Furuhashi

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© 1997 Springer-Verlag Berlin Heidelberg

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Chung, C., Reynolds, R.G. (1997). Function optimization using evolutionary programming with self-adaptive cultural algorithms. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028517

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  • DOI: https://doi.org/10.1007/BFb0028517

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63399-0

  • Online ISBN: 978-3-540-69538-7

  • eBook Packages: Springer Book Archive

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