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Evolution and Biocomputation pp 123–141Cite as

Soft genetic operators in Evolutionary Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 899))

Abstract

With this paper soft genetic operators for Evolutionary Algorithms are introduced and analyzed for multimodal continuous parameter optimization problems. A new scaling rule for multiple mutations is formalized and compared with a new step-size scaling for Evolution Strategies. The scaling of the Evolutionary Algorithm with Soft genetic operators (EASY) is compared with that of the Breeder Genetic Algorithm (BGA). A performance comparison of EASY with recently published results concerning the performance of Bayesian/Sampling and Very Fast Simulated Reannealing techniques for global optimization is given.

This work is supported by the Bundesminister für Forschung und Technologie (BMFT) as part of the project SALGON and the Deutsche Forschungsgemeinschaft (DFG) Grant Vo 493/1-1.

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Authors and Affiliations

  1. Bionics and Evolution Techniques Laboratory, Technical University Berlin, Ackerstraße 71-76, 13355, Berlin, Germany

    Hans -Michael Voigt

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  1. Hans -Michael Voigt
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Wolfgang Banzhaf Frank H. Eeckman

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© 1995 Springer-Verlag Berlin Heidelberg

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Voigt, H.M. (1995). Soft genetic operators in Evolutionary Algorithms. In: Banzhaf, W., Eeckman, F.H. (eds) Evolution and Biocomputation. Lecture Notes in Computer Science, vol 899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59046-3_8

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  • DOI: https://doi.org/10.1007/3-540-59046-3_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59046-0

  • Online ISBN: 978-3-540-49176-7

  • eBook Packages: Springer Book Archive

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