Statistics-based adaptive non-uniform mutation for genetic algorithms

Abstract

A statistics-based adaptive non-uniform mutation (SANUM) is presented for genetic algorithms (GAs), within which the probability that each gene will subject to mutation is learnt adaptively over time and over the loci. SANUM uses the statistics of the allele distribution in each locus to adaptively adjust the mutation probability of that locus. The experiment results demonstrate that SANUM performs persistently well over a range of typical test problems while the performance of traditional mutation operators with fixed rates greatly depends on the problems. SANUM represents a robust adaptive mutation that needs no advanced knowledge about the problem landscape.