A genetic algorithm for a Hamiltonian path problem: Mutation - crossover interaction

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“A genetic algorithm for a Hamiltonian path problem: Mutation - crossover interaction” by E. Ponce de León, R. Santana, and A. Ochoa. In Proceedings of the 13th ISPE/IEE International Conference on CAD/CAM Robotics and Factories of the Future 97, (Universidad Tecnológica de Pereira, Colombia), Dec. 1997, pp. 1001-1006.

Abstract

The GA method is especially useful in cases when the hypersurface, in which the optimum is searched, is of a high dimension and has many local optima. The complexity of such problems renders an exhaustive search through the space (using, for example, a grid search) useless. Due to local optima, there is a danger that direct optimization methods stop far away from the global optimum.

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BibTeX entry:

@inproceedings{Ponce_et_al:1997b,
   author = {E. Ponce de Le{\'o}n and R. Santana and A. Ochoa},
   title = {A genetic algorithm for a {H}amiltonian path problem: Mutation
	- crossover interaction},
   booktitle = {Proceedings of the 13th ISPE/IEE International Conference
	on CAD/CAM Robotics and Factories of the Future 97},
   pages = {1001-1006},
   address = {Universidad Tecnol{\'o}gica de Pereira, Colombia},
   month = dec,
   year = {1997}
}

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