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Geometric crossovers for multiway graph partitioning.

Alberto Moraglio1, Yong-Hyuk Kim, Yourim Yoon

  • 1Centre for Informatics and Systems of the University of Coimbra Polo II - Pinhal de Marrocos, 3030 Coimbra, Portugal. moraglio@dei.uc.pt

Evolutionary Computation
|November 21, 2007
PubMed
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We introduce a novel geometric crossover for multiway graph partitioning, overcoming issues with traditional methods on redundant encodings. This new approach enhances search by embedding problem-specific knowledge and ensuring feasible solutions.

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Graph Theory

Background:

  • Traditional crossover methods struggle with redundant encodings common in multiway graph partitioning.
  • Existing approaches often fail to produce feasible solutions after recombination.

Purpose of the Study:

  • To develop a representation-independent geometric crossover adaptable to various solution encodings.
  • To design a problem-specific crossover for multiway graph partitioning that embeds domain knowledge.
  • To address the feasibility challenge in recombining graph partitions.

Main Methods:

  • Developed a geometric crossover based on a labeling-independent distance to handle redundant encodings in graph partitioning.
  • Designed a novel geometric crossover for permutations with repetitions suitable for partition problems.

Related Experiment Videos

  • Combined these methods to create a superior geometric crossover.

    • Main Results:

    • The proposed labeling-independent distance is well-suited for graph partitioning, as indicated by fitness landscape correlation analysis.
    • The new geometric crossover for permutations with repetitions effectively addresses feasibility issues.
    • The combined geometric crossover demonstrates significantly improved performance.

    Conclusions:

    • Geometric crossover offers a powerful, representation-independent framework for evolutionary algorithms.
    • Problem-specific geometric crossovers can embed crucial domain knowledge, enhancing search efficiency.
    • The developed geometric crossover effectively tackles redundancy and feasibility challenges in multiway graph partitioning.