Random State Genetic Algorithm

Louis Gacôgne (1)

1 MALIRE - Machine Learning and Information Retrieval

Following earlier results, the purpose of this paper is to show a new evolutionary algorithm whose parameters are moving in ranges defined by experiments. That is to say, no parameters must be fixed at the beginning of the course of generations. Comparing the performance of two methods, we arrive to the conclusion that the random often is a better way.

Domaines: Informatique [cs]

Louis Gacôgne. Random State Genetic Algorithm. The 11th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2012, Apr 2012, Zakopane, Poland. pp.214-221, ⟨10.1007/978-3-642-29353-5_25⟩. ⟨hal-01269854⟩