A multiset genetic algorithm for the optimization of deceptive problems
António Manso, Luís Miguel Parreira Correia · 2013
MuGA is an evolutionary algorithm (EA) that represents populations as multisets, instead of the conventional collection. Such representation can be explored to adapt genetic operators in order to increase performance in difficult problems. In this paper we present an adaptation of the mutation operator, multiset wave mutation (MWM), that explores the multiset representation to apply different mutation ratios to the same chromosome, and an adaptation of the replacement operator, multiset decimation replacement (MDR) that preserves multiset representation in the main population and helps MuGA to solve hard deceptive problems. Results obtained in different deceptive functions show that pairing both operators is a robust approach with a high success ratio in most of the problems.