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Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite

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Abstract

The unbounded population multi-objective covariance matrix adaptation evolution strategy (UP-MO-CMA-ES) aims at maximizing the total hypervolume covered by all evaluated points. It adds all non-dominated solutions found to its population and employs Gaussian mutations with adaptive covariance matrices to also solve ill-conditioned problems. A novel recombination operator adapts the covariance matrices to point along the Pareto front. The UP-MO-CMA-ES is combined with a parallel exploration strategy and empirically evaluated on the bi-objective BBOB-biobj benchmark problems. Results show that the algorithm can reliably solve ill-conditioned problems as well as weakly-structured problems. However, it is less suited for the rugged multi-modal objective functions in the benchmark.
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Dates and versions

hal-01381653 , version 1 (14-10-2016)

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Oswin Krause, Tobias Glasmachers, Nikolaus Hansen, Christian Igel. Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite. GECCO'16 - Companion of Proceedings of the 2016 Genetic and Evolutionary Computation Conference, ACM, Jul 2016, Denver, United States. pp.1177-1184, ⟨10.1145/2908961.2931699⟩. ⟨hal-01381653⟩
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