TY - GEN
T1 - Uncrowded hypervolume improvement
T2 - 2019 Genetic and Evolutionary Computation Conference, GECCO 2019
AU - Touré, Cheikh
AU - Auger, Anne
AU - Hansen, Nikolaus
AU - Brockhoff, Dimo
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.
PY - 2019/7/13
Y1 - 2019/7/13
N2 - We present a framework to build a multiobjective algorithm from single-objective ones. This framework addresses the p × 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.
AB - We present a framework to build a multiobjective algorithm from single-objective ones. This framework addresses the p × 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.
KW - Hypervolume
KW - Hypervolume contribution
KW - Hypervolume improvement
KW - Multiobjective optimization
KW - Quality indicator
KW - Single-objective optimization
UR - https://www.scopus.com/pages/publications/85070613663
U2 - 10.1145/3321707.3321852
DO - 10.1145/3321707.3321852
M3 - Conference contribution
AN - SCOPUS:85070613663
T3 - GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
SP - 638
EP - 646
BT - GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
Y2 - 13 July 2019 through 17 July 2019
ER -