%0 Journal Article
%J Journal of Heuristics
%D 2004
%T Robust parameter settings for variation operators by measuring the resampling ratio: A study on binary constraint satisfaction problems
%A van Hemert, J. I.
%A Bäck, T.
%K constraint satisfaction
%K evolutionary computation
%K resampling ratio
%X In this article, we try to provide insight into the consequence of mutation and crossover rates when solving binary constraint satisfaction problems. This insight is based on a measurement of the space searched by an evolutionary algorithm. From data empirically acquired we describe the relation between the success ratio and the searched space. This is achieved using the resampling ratio, which is a measure for the amount of points revisited by a search algorithm. This relation is based on combinations of parameter settings for the variation operators. We then show that the resampling ratio is useful for identifying the quality of parameter settings, and provide a range that corresponds to robust parameter settings.
%B Journal of Heuristics
%V 10
%P 629--640
%G eng
%9 article
%0 Conference Paper
%B Springer Lecture Notes on Computer Science
%D 2002
%T Measuring the Searched Space to Guide Efficiency: The Principle and Evidence on Constraint Satisfaction
%A van Hemert, J. I.
%A Bäck, T.
%E J. J. Merelo
%E A. Panagiotis
%E H.-G. Beyer
%E Jos{\'e}-Luis Fern{\'a}ndez-Villaca{\~n}as
%E Hans-Paul Schwefel
%K constraint satisfaction
%K resampling ratio
%X In this paper we present a new tool to measure the efficiency of evolutionary algorithms by storing the whole searched space of a run, a process whereby we gain insight into the number of distinct points in the state space an algorithm has visited as opposed to the number of function evaluations done within the run. This investigation demonstrates a certain inefficiency of the classical mutation operator with mutation-rate 1/l, where l is the dimension of the state space. Furthermore we present a model for predicting this inefficiency and verify it empirically using the new tool on binary constraint satisfaction problems.
%B Springer Lecture Notes on Computer Science
%I Springer-Verlag, Berlin
%P 23--32
%@ 3-540-44139-5
%G eng
%9 inproceedings