70 citations · 107 across the 5 of their papers we have counts for
5 papers
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
Analyzing design principles for competitive evolution strategies in constrained search spaces
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 t…
A Linear Constrained Optimization Benchmark For Probabilistic Search Algorithms: The Rotated Klee-Minty Problem
Michael Hellwig, Hans-Georg Beyer
The development, assessment, and comparison of randomized search algorithms heavily rely on benchmarking. Regarding the domain of constrained optimization, the number of currently…
A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints
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. The proposed alg…
Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review
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. This paper presen…