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Deterministic Multi-step Crossover Fusion: A Handy Crossover Composition for GAs

  • Kokolo Ikeda
  • Shigenobu Kobayashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

Multi-step crossover fusion (MSXF) is a promising crossover method using only the neighborhood structure and the distance measure, when heuristic crossovers are hardly introduced. However, MSXF works unsteadily according to the temperature parameter, like as Simulated Annealing. In this paper, we introduce deterministic multi-step crossover fusion (dMSXF) to take this parameter away. Instead of the probabilistic acceptance of MSXF, neighbors are restricted to be closer to the goal solution, the best candidate of them is selected definitely as the next step solution. The performance of dMSXF is tested on 1max problem and Traveling Salesman Problem, and its superiority to conventional methods, e.g. uniform crossover, is shown.

Keywords

Genetic Algorithm Simulated Annealing Traveling Salesman Problem Travel Salesman Problem Neighborhood Structure 
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

  • Kokolo Ikeda
    • 1
  • Shigenobu Kobayashi
    • 1
  1. 1.Interdisciplinary Graduate School of Science and EngineeringTokyo Institute of TechnologyTokyo

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