Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
Antonio LaTorre, José Manuel Peña, Vı́ctor Robles, Santiago Muelas · 2008
The correct choice of an evolutionary algorithm, a genetic repre-sentation 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 opti-mization 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 bal-ances 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 strate-gies), and configures appropriate sampling sizes. This formalism has been applied to a well-known permutation problem, the traveling salesman problem (TSP). The results on sev-eral instances of this problem show that most of the combined tech-niques outperform the results obtained by single ones.