A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints

Patrick Spettel; Hans-Georg Beyer; Michael Hellwig

doi:10.1109/tevc.2018.2871944

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arXiv · 1806.05845

A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints

Patrick Spettel·Hans-Georg Beyer·Michael Hellwig

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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.

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BibTeX RIS

Patrick Spettel, Hans-Georg Beyer, Michael Hellwig. 2018-06-15. A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints. https://doi.org/10.1109/tevc.2018.2871944

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