A Hybrid Genetic Algorithm with Boltzmann Convergence Properties

W. C. Jackson () and J. D. Norgard
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W. C. Jackson: SpaceDev, Inc.
J. D. Norgard: University of Colorado

Journal of Optimization Theory and Applications, 2008, vol. 136, issue 3, No 8, 443 pages

Abstract: 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.

Keywords: Combinatorial optimization; Genetic algorithms; Hybrid algorithms; Simulated annealing (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/s10957-007-9308-8

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