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A Feasibility-Preserving Crossover and Mutation Operator for Constrained Combinatorial Problems

  • Martin Lukasiewycz
  • Michael Glaß
  • Jürgen Teich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

This paper presents a feasibility-preserving crossover and mutation operator for evolutionary algorithms for constrained combinatorial problems. This novel operator is driven by an adapted Pseudo-Boolean solver that guarantees feasible offspring solutions. Hence, this allows the evolutionary algorithm to focus on the optimization of the objectives instead of searching for feasible solutions. Based on a proposed scalable testsuite, six specific testcases are introduced that allow a sound comparison of the feasibility-preserving operator to known methods. The experimental results show that the introduced approach is superior to common methods and competitive to a recent state-of-the-art decoding technique.

Keywords

Feasible Solution Evolutionary Algorithm Mutation Operator Combinatorial Problem Infeasible Solution 
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 2008

Authors and Affiliations

  • Martin Lukasiewycz
    • 1
  • Michael Glaß
    • 1
  • Jürgen Teich
    • 1
  1. 1.Hardware-Software-Co-Design, Department of Computer Science 12University of Erlangen-NurembergGermany

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