The theory of evolution strategies
The theory of evolution strategies
Noisy Local Optimization with Evolution Strategies
Noisy Local Optimization with Evolution Strategies
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Constraint-handling techniques used with evolutionary algorithms
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
On the Behaviour of the (1+1)-ES for a Simple Constrained Problem
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Adaptive Encoding: How to Render Search Coordinate System Invariant
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the behaviour of the (1,λ)-es for a simple constrained problem
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Analysis of a repair mechanism for the (1,λ)-ES applied to a simple constrained problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Test-case generator for nonlinear continuous parameter optimizationtechniques
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
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We study the behaviour of multi-recombination evolution strategies for the problem of maximising a linear function with a single linear constraint. Two variants of the algorithm are considered: a strategy that resamples infeasible candidate solutions and one that applies a simple repair mechanism. Integral expressions that describe the strategies' one-generation behaviour are derived and used in a simple zeroth order model for the steady state attained when operating with constant step size. Applied to the analysis of cumulative step size adaptation, the approach provides an intuitive explanation for the qualitative difference in the algorithm variants' behaviour. The findings have implications for the design of constraint handling techniques to be used in connection with cumulative step size adaptation.