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Proceedings Paper

Large ratios of mutation to crossover: the example of the traveling salesman problem
Author(s): David John Nettleton; Roberto Garigliano
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Paper Abstract

Genetic algorithms have recently been successfully applied to a wide range of problems. These often have search spaces that are very large, very complex, or both and are unsuitable for standard search algorithms such as hill climbing. The operators used in producing successive generations are usually those of crossover and mutation. The crossover operator is normally used in producing the majority of a generation while mutation acts as a background process. This paper examines the use of high amounts of mutation and gives the example of a genetic algorithm applied to the travelling salesman problem. This shows that high amounts of mutation need not ruin the algorithms convergence to optimal solutions.

Paper Details

Date Published: 1 September 1993
PDF: 10 pages
Proc. SPIE 1962, Adaptive and Learning Systems II, (1 September 1993); doi: 10.1117/12.150578
Show Author Affiliations
David John Nettleton, Univ. of Durham (United Kingdom)
Roberto Garigliano, Univ. of Durham (United Kingdom)


Published in SPIE Proceedings Vol. 1962:
Adaptive and Learning Systems II
Firooz A. Sadjadi, Editor(s)

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