A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise

Authors:
Hans-George Beyer;Dirk V. Arnold;Silja Meyer-Nieberg
Affiliations:
Department of Computer Science XI, University of Dortmund, Dortmund, Germany D-44221;Faculty of Computer Science, Dalhousie University, Halifax, Canada B3H 1W5;Department of Computer Science XI, University of Dortmund, Dortmund, Germany D-44221
Venue:
Genetic Programming and Evolvable Machines
Year:
2005

Citing 14
Cited 3

The theory of evolution strategies

The theory of evolution strategies
Numerical Optimization of Computer Models

Numerical Optimization of Computer Models
Evolutionary Optimization in Dynamic Environments

Evolutionary Optimization in Dynamic Environments
Noisy Local Optimization with Evolution Strategies

Noisy Local Optimization with Evolution Strategies
A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise

Computational Optimization and Applications
Performance analysis of evolution strategies with multi-recombination in high-dimensional RN-search spaces disturbed by noise

Theoretical Computer Science
Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies

Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies

Evolutionary Computation
A derandomized approach to self-adaptation of evolution strategies

Evolutionary Computation
The steady state behavior of (µ/µI, λ)-ES on ellipsoidal fitness models disturbed by noise

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Evolutionary computation: comments on the history and current state

IEEE Transactions on Evolutionary Computation
Genetic algorithms with a robust solution searching scheme

IEEE Transactions on Evolutionary Computation
Robust design of multilayer optical coatings by means ofevolutionary algorithms

IEEE Transactions on Evolutionary Computation
On self-adaptive features in real-parameter evolutionary algorithms

IEEE Transactions on Evolutionary Computation

Why noise may be good: additive noise on the sharp ridge

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge

Natural Computing: an international journal
Performance analysis of the simultaneous perturbation stochastic approximation algorithm on the noisy sphere model

Theoretical Computer Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

Differential-geometric methods are applied to derive steady state conditions for the (驴/驴I,驴)-ES on the general quadratic test function disturbed by fitness noise of constant strength. A new approach for estimating the expected final fitness deviation observed under such conditions is presented. The theoretical results obtained are compared with real ES runs, showing a surprisingly excellent agreement.