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GA-easy and GA-hard constraint satisfaction problems

  • Ágoston Eiben
  • Paul-Erik Raué
  • Zsófia Ruttkay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 923)

Abstract

In this paper we discuss the possibilities of applying genetic algorithms (GA) for solving constraint satisfaction problems (CSP). We point out how the greediness of deterministic classical CSP solving techniques can be counterbalanced by the random mechanisms of GAs. We tested our ideas by running experiments on four different CSPs: N-queens, graph 3-colouring, the traffic lights and the Zebra problem. Three of the problems have proven to be GA-easy, and even for the GA-hard one the performance of the GA could be boosted by techniques familiar in classical methods. Thus GAs are promising tools for solving CSPs. In the discussion, we address the issues of non-solvable CSPs and the generation of all the solutions.

Keywords

Genetic Algorithm Genetic Operator Constraint Satisfaction Problem Traffic Light Local Repair 
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 1995

Authors and Affiliations

  • Ágoston Eiben
    • 1
  • Paul-Erik Raué
    • 1
  • Zsófia Ruttkay
    • 1
  1. 1.Dept. of Mathematics and Computer ScienceVrije Universiteit AmsterdamHV AmsterdamThe Netherlands

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