A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints
Abstract
This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a linear manifold defined by the constraints. The objective function is only evaluated at feasible search points (interior point method). This is a property often required in application domains such as simulation optimization and finite element methods. The algorithm is tested on a variety of different test problems revealing considerable results.
Cite
@article{arxiv.1806.05845,
title = {A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints},
author = {Patrick Spettel and Hans-Georg Beyer and Michael Hellwig},
journal= {arXiv preprint arXiv:1806.05845},
year = {2018}
}
Comments
This is a PREPRINT of an article accepted by IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION. >>> COPYRIGHT 2018 IEEE <<< Manuscript received Jan, 2018; revised Jun, 2018; accepted Sep, 2018. Due to size limitations, this manuscript comprises figures with reduced resolution. The work was supported by the Austrian Science Fund FWF under grant P29651-N32. Content: 10 pages + supplementary material