Geometric crossover for multiway graph partitioning

  • Authors:

  • Yong-Hyuk Kim;Yourim Yoon;Alberto Moraglio;Byung-Ro Moon

  • Affiliations:

  • Seoul National University, Seoul, Korea;Seoul National University, Seoul, Korea;University of Essex, Colchester, UK;Seoul National University, Seoul, Korea

  • Venue:
  • Proceedings of the 8th annual conference on Genetic and evolutionary computation
  • Year:
  • 2006

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Abstract

Geometric crossover is a representation-independent generalization of the traditional crossover defined using the distance of the solution space. Using a distance tailored to the problem at hand, the formal definition of geometric crossover allows to design new problem-specific crossovers that embed problem-knowledge in the search. The standard encoding for multiway graph partitioning is highly redundant: each solution has a number of representations, one for each way of labeling the represented partition. Traditional crossover does not perform well on redundant encodings. We propose a new geometric crossover for graph partitioning based on a labeling-independent distance that filters the redundancy of the encoding. A correlation analysis of the fitness landscape based on this distance shows that it is well suited to graph partitioning. Our new genetic algorithm outperforms existing ones.