no code implementations • 8 May 2024 • Michael Hellwig, Hans-Georg Beyer
In the context of the 2018 IEEE Congress of Evolutionary Computation, the Matrix Adaptation Evolution Strategy for constrained optimization turned out to be notably successful in the competition on constrained single objective real-parameter optimization.
no code implementations • 23 Jan 2019 • Patrick Spettel, Hans-Georg Beyer
Based on that, expressions for the steady state of the mean value iterative system are derived.
no code implementations • 15 Dec 2018 • Patrick Spettel, Hans-Georg Beyer
Approximate deterministic evolution equations are formulated for analyzing the strategy's dynamics.
1 code implementation • 26 Jul 2018 • Michael Hellwig, Hans-Georg Beyer
The development, assessment, and comparison of randomized search algorithms heavily rely on benchmarking.
no code implementations • 15 Jun 2018 • Patrick Spettel, Hans-Georg Beyer, Michael Hellwig
This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints.
no code implementations • 12 Jun 2018 • Michael Hellwig, Hans-Georg Beyer
Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas.
2 code implementations • 18 May 2017 • Ilya Loshchilov, Tobias Glasmachers, Hans-Georg Beyer
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popular method to deal with nonconvex and/or stochastic optimization problems when the gradient information is not available.