Conditioning, halting criteria and choosing λ

Authors:
Olivier Teytaud
Affiliations:
Equipe TAO, Inria, LRI, UMR, CNRS, Université Paris-Sud, Orsay Cedex, France
Venue:
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Year:
2007

Citing 6
Cited 0

Global convergence for evolution strategies in spherical problems: some simple proofs and difficulties

Theoretical Computer Science
Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
Learning probability distributions in continuous evolutionary algorithms– a comparative review

Natural Computing: an international journal
Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains

Theoretical Computer Science
Reconsidering the progress rate theory for evolution strategies in finite dimensions

Proceedings of the 8th annual conference on Genetic and evolutionary computation
General lower bounds for evolutionary algorithms

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature

Quantified Score

Hi-index	0.00

Visualization

Abstract

We show the convergence of 1+λ-ES with standard step-size update-rules on a large family of fitness functions without any convexityassumption or quasi-convexity assumptions ([3,6]). The result providesa rule for choosing λ and shows the consistency of halting criteria basedon thresholds on the step-size. The family of functions under work is defined through a condition-number that generalizes usual condition-numbers in a manner that onlydepends on level-sets. We consider that the definition of this condition-number is the relevant one for evolutionary algorithms; in particular,global convergence results without convexity or quasi-convexity assumptionsare proved when this condition-number is finite.