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A Hybrid Genetic Algorithm with Boltzmann Convergence Properties

Author

Listed:
  • W. C. Jackson

    (SpaceDev, Inc.)

  • J. D. Norgard

    (University of Colorado)

Abstract

Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems. However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm.

Suggested Citation

  • W. C. Jackson & J. D. Norgard, 2008. "A Hybrid Genetic Algorithm with Boltzmann Convergence Properties," Journal of Optimization Theory and Applications, Springer, vol. 136(3), pages 431-443, March.
    • Handle: RePEc:spr:joptap:v:136:y:2008:i:3:d:10.1007_s10957-007-9308-8
    DOI: 10.1007/s10957-007-9308-8
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    References listed on IDEAS

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    1. Grohall, Guenther & Jung, Juergen, 2003. "Multiple Objective Step Function Maximization with Genetic Algorithms and Simulated Annealing," Economics Series 141, Institute for Advanced Studies.
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