A Racing Algorithm for Configuring Metaheuristics
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Tuning Metaheuristics: A Machine Learning Perspective
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HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
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PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper, we experimentally explore the influence tuned parameter settings have on an IPOP-CMA-ES variant that uses a maximum bound on the population size. We followed our earlier work, where we exposed seven parameters that control parameters of IPOP-CMA-ES, and tune them by applying irace, an automatic algorithm configuration tool. A comparison of the tuned to the default settings on the BBOB benchmark shows that for difficult problems such as multi-modal functions with weak global structure, the tuned parameter settings can result in significant improvements over the default settings.