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Measuring the Searched Space to Guide Efficiency: The Principle and Evidence on Constraint Satisfaction

  • Jano I. van Hemert
  • Thomas Bäck
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

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.

Keywords

State Space Mutation Rate Evolutionary Algorithm Problem Instance Mutation Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jano I. van Hemert
    • 1
  • Thomas Bäck
    • 1
    • 2
  1. 1.Leiden Institute of Advanced Computer ScienceLeiden UniversityLeiden
  2. 2.Nutech Solutions GmbHDortmund

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