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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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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DOI: https://doi.org/10.1007/978-3-319-08377-3_35
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