Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Information Sciences: an International Journal
Ensemble of niching algorithms
Information Sciences: an International Journal
Network crossover performance on NK landscapes and deceptive problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Generalized crowding for genetic algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Fecundity and selectivity in evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A multiset genetic algorithm for real coded problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
MuGA: multiset genetic algorithm
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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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.