Learning Individual Mating Preferences
Abstract
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.
Recommended Citation
L. M. Guntly and D. R. Tauritz, "Learning Individual Mating Preferences," Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, GECCO'11, pp. 1069 - 1076, Association for Computing Machinery (ACM), Jan 2011.
The definitive version is available at https://doi.org/10.1145/2001576.2001721
Meeting Name
13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 (2011: Jul. 12-16, Dublin, Ireland)
Department(s)
Computer Science
Sponsor(s)
Missouri University of Science and Technology. Natural Computation Laboratory
Keywords and Phrases
Evolutionary Algorithm; Mate Selection; Reinforcement Learning
International Standard Book Number (ISBN)
978-1450305570
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2011 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 Jan 2011
