Adaptive non-uniform mutation based on statistics for genetic algorithms
As a meta-heuristic search algorithm based on mechanisms abstracted from population genetics, the genetic algorithm (GA) implicitly maintains the statistics about the search space through the population. This implicit statistics can be explicitly used to enhance GA's performance. In this paper, a statistics-based adaptive non-uniform mutation (SANUM) is proposed. SANUM uses the statistics information of the allele distribution in each locus to adaptively adjust the mutation operation. Our preliminary experiments show that SANUM outperforms traditional bit flip mutation across a representative set set of test problems.
Citation : Yang. S. (2002) Adaptive non-uniform mutation based on statistics for genetic algorithms. In Erick Cantu-Paz (editor), Late-Breaking Papers at the 2002 Genetic and Evolutionary Computation Conference, pp. 490-495
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes