Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
Artificial Intelligence Review
Cost Based Operator Rate Adaption: An Investigation
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
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Genetic algorithms are a frequently used method for search and optimization problem solving. They have been applied very successfully to many NP-hard problems, among which the traveling salesman problem, which is also considered in this paper, is one of the most famous representative ones. A genetic algorithm usually makes use only of single mutation and a single crossover operator. However, three modes for determination which of the double crossover and mutation operators should be used in a given moment are presented. It has also been tested if there is a positive impact on the performance if double genetic operators are used. Experimental analysis conducted on several instances of the symmetric traveling salesman problem showed that it is possible to achieve better results by adaptively adjusting the usage of double operators, rather than by combining any single genetic operators.