Using assortative mating in genetic algorithms for vector quantization problems
Proceedings of the 2001 ACM symposium on Applied computing
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Improving the Performance of Genetic Algorithms in Classifier Systems
Proceedings of the 1st International Conference on Genetic Algorithms
Enhancing GA Performance through Crossover Prohibitions Based on Ancestry
Proceedings of the 6th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Reinforcement learning in steady-state cellular genetic algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Peer-to-peer evolutionary algorithms with adaptive autonomous selection
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Learning offspring optimizing mate selection
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Self-adjusting the intensity of assortative mating in genetic algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Self-adaptive mate choice for cluster geometry optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Mate selection is a key step in evolutionary algorithms which traditionally has been panmictic and based solely on fitness. Various mate selection techniques have been published which show improved performance due to the introduction of mate restrictions or the use of genotypic/phenotypic features. Those techniques typically suffer from two major shortcomings: (1) they are fixed for the entire evolutionary run, which is suboptimal because problem specific mate selection may be expected to outperform general purpose mate selection and because the best mate selection configuration may be dependent on the state of the evolutionary run, and (2) they require problem specific tuning in order to obtain good performance, which often is a time consuming manual process. This paper introduces two versions of Learning Individual Mating Preferences (LIMP), a novel mate selection technique in which characteristics of good mates are learned during the evolutionary process. Centralized LIMP (C-LIMP) learns at the population level, while Decentralized LIMP (D-LIMP) learns at the individual level. Results are presented showing D-LIMP to outperform a traditional genetic algorithm (TGA), the Variable Dissortative Mating Genetic Algorithm (VDMGA), and C-LIMP on the DTRAP and MAXSAT benchmark problems, while both LIMP techniques perform comparably to VDMGA on NK Landscapes.