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Found 7 papers, 2 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.
Analysis of the $(μ/μ_I,λ)$-CSA-ES with Repair by Projection Applied to a Conically Constrained Problem
Based on that, expressions for the steady state of the mean value iterative system are derived.
Analysis of the $(μ/μ_I,λ)$-$σ$-Self-Adaptation Evolution Strategy with Repair by Projection Applied to a Conically Constrained Problem
Approximate deterministic evolution equations are formulated for analyzing the strategy's dynamics.
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.
Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization
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.
