Annealing a Genetic Algorithm for Constrained Optimization

F. Mendivil () and R. Shonkwiler ()
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F. Mendivil: Acadia University
R. Shonkwiler: Georgia Institute of Technology

Journal of Optimization Theory and Applications, 2010, vol. 147, issue 2, No 11, 395-410

Abstract: 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.

Keywords: Genetic algorithms; Constrained optimization; Simulated annealing; Markov chain; Convergence proofs (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s10957-010-9716-z

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