Abstract
This chapter studies how to deal with infeasible solutions when evolutionary algorithms are used for constrained optimization. We derive sufficient and necessary conditions to judge the usefulness of infeasible solutions in concrete problems. We then disclose that Pareto optimization, transforming the original constrained optimization problem into a bi-objective optimization problem, is probably better than the commonly employed penalty method and the greedy method. Its effectiveness is moreover verified in machine learning tasks.
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© 2019 Springer Nature Singapore Pte Ltd.
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Zhou, ZH., Yu, Y., Qian, C. (2019). Constrained Optimization. In: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-13-5956-9_12
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DOI: https://doi.org/10.1007/978-981-13-5956-9_12
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5955-2
Online ISBN: 978-981-13-5956-9
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