A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
The “molecular” traveling salesman
Biological Cybernetics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Quality measures to adapt the participation in MOS
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Learning hybridization strategies in evolutionary algorithms
Intelligent Data Analysis
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The correct choice of an evolutionary algorithm, a genetic representation for the problem being solved (as well as their associated variation operators) and the appropriate values for the parameters of the algorithm is a hard task and it is often considered as an optimization problem itself. In this contribution, we propose a new theoretical formalism, called Multiple Offspring Sampling (MOS). This new technique combines different evolutionary approaches taking advantage of the benefits provided by each of them. MOS dynamically balances the participation of different mechanisms to spawn the new offspring population, according to the benefits provided by each of them in previous generations. This approach evaluates multiple offspring generation methods (for example different coding strategies), and configures appropriate sampling sizes. This formalism has been applied to a well-known permutation problem, the traveling salesman problem (TSP). The results on several instances of this problem show that most of the combined techniques outperform the results obtained by single ones.