Computer Science – Neural and Evolutionary Computing
Scientific paper
2012-03-14
IJES, International Journal of Emerging Sciences , 2(1), 61-77, March 2012
Computer Science
Neural and Evolutionary Computing
ISSN: 2222-4254
Scientific paper
The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Travelling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.
Abdoun Otman
Abouchabaka Jaafar
Tajani Chakir
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