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An Evolutionary Algorithm Based on Stochastic Weighted Learning for Constrained Optimization

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

In this paper, we propose an evolutionary algorithm based on a single operator called stochastic weighted learning, i.e., each individual will learn from other individuals specified with stochastic weight coefficients in each generation, for constrained optimization. For handling equality and inequality constraints, the proposed algorithm introduces a learning rate adapting technique combined with a fitness comparison schema. Experiment results on a set of benchmark problems show the efficiency of the algorithm.

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© 2005 Springer-Verlag Berlin Heidelberg

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Ye, J., Liu, X., Han, L. (2005). An Evolutionary Algorithm Based on Stochastic Weighted Learning for Constrained Optimization. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_150

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  • DOI: https://doi.org/10.1007/11539117_150

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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