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Found 4 papers, 1 papers with code
Analyzing design principles for competitive evolution strategies in constrained search spaces
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.
A Linear Constrained Optimization Benchmark For Probabilistic Search Algorithms: The Rotated Klee-Minty Problem
The development, assessment, and comparison of randomized search algorithms heavily rely on benchmarking.
A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints
This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints.
Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review
Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas.
