Instinct-based mating in genetic algorithms applied to the tuning of 1-NN classifiers

Thiago Quirino, Miroslav Kubat, Nicholas J. Bryan

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Article number5374400
Pages (from-to)1724-1737
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
StatePublished - 2010


  • Genetic algorithm
  • mating strategies
  • multiobjective optimization
  • nearest-neighbor classifiers

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Computer Science Applications


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