Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
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
Reconsidering the progress rate theory for evolution strategies in finite dimensions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for constrained parameter optimization problems
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
On the behaviour of evolution strategies optimising cigar functions
Evolutionary Computation
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
On the behaviour of the (1,λ)-σSA-ES for a constrained linear problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
| Hi-index | 0.00 |
We consider a conically constrained optimisation problem where the optimal solution lies at the apex of the cone and study the behaviour of a (1,λ)-ES that handles constraints by resampling infeasible candidate solutions. Expressions that describe the strategy's single-step behaviour are derived. Assuming that the mutation strength is adapted in a scale-invariant manner, a simple zeroth-order model is used to determine the speed of convergence of the strategy. We then derive expressions that approximately characterise the step size and convergence rate attained when using cumulative step size adaptation and compare the values with optimal ones.