Abstract
This chapter presents the convergence-based analysis approach for analyzing the running time complexity of evolutionary algorithms, which is derived from bridging two fundamental theoretical issues. The approach is applied to show the exponential lower bound of the expected running time for (1+1)-EA and randomized local search solving the constrained Trap problem.
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© 2019 Springer Nature Singapore Pte Ltd.
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Zhou, ZH., Yu, Y., Qian, C. (2019). Running Time Analysis: Convergence-based Analysis. In: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-13-5956-9_3
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DOI: https://doi.org/10.1007/978-981-13-5956-9_3
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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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