Issue No. 12 - December (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.211
Thiago Quirino , University of Miami, Coral Gables
Miroslav Kubat , University of Miami, Coral Gables
Nicholas J. Bryan , Stanford University, Palo Alto
The behavior of the genetic algorithm (GA), a popular approach to search and optimization problems, is known to depend, among other factors, on the fitness function formula, the recombination operator, and the mutation operator. What has received less attention is the impact of the mating strategy that selects the chromosomes to be paired for recombination. Existing GA implementations mostly choose them probabilistically, according to their fitness function values, but we show that more sophisticated mating strategies can not only accelerate the search, but perhaps even improve the quality of the GA-generated solution. In our implementation, we took inspiration from the "opposites-attract” principle that is so common in nature. As a testbed, we chose the problem of 1-NN classifier tuning where genetic solutions have been employed before, and are thus well-understood by the research community. We propose three "instinct-based” mating strategies and experimentally investigate their behaviors.
Genetic algorithm, mating strategies, multiobjective optimization, nearest-neighbor classifiers.
T. Quirino, M. Kubat and N. J. Bryan, "Instinct-Based Mating in Genetic Algorithms Applied to the Tuning of 1-NN Classifiers," in IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 1724-1737, 2009.