Impact of Double Operators on the Performance of a Genetic Algorithm for Solving the Traveling Salesman Problem
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.
KeywordsCombination genetic algorithm genetic operators synergy traveling salesman problem
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