01316nas a2200169 4500008004100000245014000041210006900181300001300250490000700263520073000270653002801000653002901028653002101057100001601078700001301094856003901107 2004 eng d00aRobust parameter settings for variation operators by measuring the resampling ratio: A study on binary constraint satisfaction problems0 aRobust parameter settings for variation operators by measuring t a629--6400 v103 aIn 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.10aconstraint satisfaction10aevolutionary computation10aresampling ratio1 aHemert, J I1 aBäck, T uhttp://research.nesc.ac.uk/node/1801378nas a2200229 4500008004100000020001800041245010800059210006900167260002800236300001100264520063400275653002800909653002100937100001600958700001300974700001600987700001801003700001601021700004801037700002401085856003901109 2002 eng d a3-540-44139-500aMeasuring the Searched Space to Guide Efficiency: The Principle and Evidence on Constraint Satisfaction0 aMeasuring the Searched Space to Guide Efficiency The Principle a bSpringer-Verlag, Berlin a23--323 aIn 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.10aconstraint satisfaction10aresampling ratio1 aHemert, J I1 aBäck, T1 aMerelo, J J1 aPanagiotis, A1 aBeyer, H -G1 aFern{\'a}ndez-Villaca{\~n}as, Jos{\'e}-Luis1 aSchwefel, Hans-Paul uhttp://research.nesc.ac.uk/node/27