Abstract
This chapter studies the approximation performance of evolutionary algorithms through the SEIP framework. SEIP adopts an isolation function to manage competition among solutions and offers a general characterization of approximation behaviors. The framework is applied to the set cover problem, delivering an \( H_m \)-approximation ratio that matches the asymptotic lower bound.
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© 2019 Springer Nature Singapore Pte Ltd.
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Zhou, ZH., Yu, Y., Qian, C. (2019). Approximation Analysis: SEIP. In: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-13-5956-9_6
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DOI: https://doi.org/10.1007/978-981-13-5956-9_6
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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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