Large ratios of mutation to crossover: the example of the traveling salesman problem
David John Nettleton, Roberto Garigliano · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
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.