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Advances in Global Optimization pp 355–362Cite as

A Filter-Genetic Algorithm for Constrained Optimization Problems

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 95))

Abstract

A filter-genetic method for constrained optimization problems is presented. It uses the filter technique instead of a fitness function to determine the merits of individuals. The method not only ensures the optimization of the offspring, but also avoids selecting the penalty parameter of a penalty function, which often leads to computational instability. And the numerical results are listed in the end.

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Acknowledgment

This research was supported by the National Natural Science Foundation of China (No: 11271128).

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Authors and Affiliations

  1. Department of Mathematics, East China university of science and technology, Shanghai, 200237, China

    Junjie Tang & Wei Wang

Authors
  1. Junjie Tang
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  2. Wei Wang
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Corresponding author

Correspondence to Wei Wang .

Editor information

Editors and Affiliations

  1. School of Science, Information Technology and Engineering, Federation University Australia, Ballarat, Victoria, Australia

    David Gao

  2. School Science, Inform., Tech. & Engg., Federation University Australia, Ballarat, Victoria, Australia

    Ning Ruan

  3. Dept. of Mathematical Sciences, Tsinghua University, Beijing, China

    Wenxun Xing

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Tang, J., Wang, W. (2015). A Filter-Genetic Algorithm for Constrained Optimization Problems. In: Gao, D., Ruan, N., Xing, W. (eds) Advances in Global Optimization. Springer Proceedings in Mathematics & Statistics, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-08377-3_35

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