Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework
Optimization and Control
2019-04-19 v1 Neural and Evolutionary Computing
Abstract
We present a framework to build a multiobjective algorithm from single-objective ones. This framework addresses the -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 fixed solutions. Crucially, dominated solutions minimize their distance to the empirical Pareto front defined by these 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.
Cite
@article{arxiv.1904.08823,
title = {Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework},
author = {Cheikh Touré and Nikolaus Hansen and Anne Auger and Dimo Brockhoff},
journal= {arXiv preprint arXiv:1904.08823},
year = {2019}
}