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- W4288282912 abstract "We present a framework to build a multiobjective algorithm from single-objective ones. This framework addresses the $p times n$-dimensional problem of finding p solutions in an n-dimensional search space, maximizing an indicator by dynamic subspace optimization. Each single-objective algorithm optimizes the indicator function given $p - 1$ fixed solutions. Crucially, dominated solutions minimize their distance to the empirical Pareto front defined by these $p - 1$ solutions. We instantiate the framework with CMA-ES as single-objective optimizer. The new algorithm, COMO-CMA-ES, is empirically shown to converge linearly on bi-objective convex-quadratic problems and is compared to MO-CMA-ES, NSGA-II and SMS-EMOA." @default.
- W4288282912 created "2022-07-28" @default.
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- W4288282912 date "2019-07-13" @default.
- W4288282912 modified "2023-09-27" @default.
- W4288282912 title "Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework" @default.
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- W4288282912 doi "https://doi.org/10.1145/3321707.3321852" @default.
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