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Annealing a Genetic Algorithm for Constrained Optimization

Author

Listed:
  • F. Mendivil

    (Acadia University)

  • R. Shonkwiler

    (Georgia Institute of Technology)

Abstract

In this paper, we adapt a genetic algorithm for constrained optimization problems. We use a dynamic penalty approach along with some form of annealing, thus forcing the search to concentrate on feasible solutions as the algorithm progresses. We suggest two different general-purpose methods for guaranteeing convergence to a globally optimal (feasible) solution, neither of which makes any assumptions on the structure of the optimization problem. The former involves modifying the GA evolution operators to yield a Boltzmann-type distribution on populations. The latter incorporates a dynamic penalty along with a slow annealing of acceptance probabilities. We prove that, with probability one, both of these methods will converge to a globally optimal feasible state.

Suggested Citation

  • F. Mendivil & R. Shonkwiler, 2010. "Annealing a Genetic Algorithm for Constrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 147(2), pages 395-410, November.
    • Handle: RePEc:spr:joptap:v:147:y:2010:i:2:d:10.1007_s10957-010-9716-z
    DOI: 10.1007/s10957-010-9716-z
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    References listed on IDEAS

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    1. Jeffrey W. Ohlmann & Barrett W. Thomas, 2007. "A Compressed-Annealing Heuristic for the Traveling Salesman Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 80-90, February.
    2. Jeffrey W. Ohlmann & James C. Bean & Shane G. Henderson, 2004. "Convergence in Probability of Compressed Annealing," Mathematics of Operations Research, INFORMS, vol. 29(4), pages 837-860, November.
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