Ants can learn from the opposite

Nicolás Rojas-Morales, Riff R. María-Cristina, Elizabeth Montero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this work we present different learning strategies focused on detecting candidate solutions that are not interesting to be explored by a metaheuristic, in terms of evaluation function. We include a first step before the metaheuris-tic. The information obtained from this step is given to the metaheuristic, for visiting candidate solutions that are more promising in terms of their quality. The goal of using these strategies is to learn about candidate solutions that can be discarded from the search space, and thus to improve the search of the metaheuristic. We present two new strategies that differ on how the solutions can be constructed in an opposite way. Our approach is evaluated using Ant Solver, a well-known ant based algorithm for solving Constraint Satisfaction Problems. We show promising results that make our solution as good approach to apply in other metaheuristics.

Original languageEnglish
Title of host publicationGECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
EditorsTobias Friedrich
PublisherAssociation for Computing Machinery, Inc
Pages389-396
Number of pages8
ISBN (Electronic)9781450342063
DOIs
Publication statusPublished - 20 Jul 2016
Event2016 Genetic and Evolutionary Computation Conference, GECCO 2016 - Denver, United States
Duration: 20 Jul 201624 Jul 2016

Conference

Conference2016 Genetic and Evolutionary Computation Conference, GECCO 2016
CountryUnited States
CityDenver
Period20/07/1624/07/16

Keywords

  • Ant algorithms
  • Antipheromone
  • Negative pheromone
  • Opposite learning strategies

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Software

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  • Cite this

    Rojas-Morales, N., María-Cristina, R. R., & Montero, E. (2016). Ants can learn from the opposite. In T. Friedrich (Ed.), GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference (pp. 389-396). Association for Computing Machinery, Inc. https://doi.org/10.1145/2908812.2908927