Abstract
This chapter studies the influence of noise on evolutionary algorithms. We disclose that the noise is not always bad. For hard problems, noise can be helpful, while for easy problems, it can be harmful. The findings are verified in the experiments. We also prove that the two common strategies, i.e., threshold selection and sampling, can bring robustness against noise when it is harmful.
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© 2019 Springer Nature Singapore Pte Ltd.
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Zhou, ZH., Yu, Y., Qian, C. (2019). Inaccurate Fitness Evaluation. In: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-13-5956-9_10
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DOI: https://doi.org/10.1007/978-981-13-5956-9_10
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Online ISBN: 978-981-13-5956-9
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