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Integrating Local Search within neat-GP

Published:20 July 2016Publication History

ABSTRACT

There are two important limitations of standard tree-based genetic programming (GP). First, GP tends to evolve unnecessarily large programs, what is referred to as bloat. Second, it uses inefficient search operators that operate at the syntax level. The first problem has been the subject of a fair amount of research over the years. Regarding the second problem, one approach is to use alternative search operators, for instance geometric semantic operators. However, another approach is to introduce greedy local search strategies, combining the syntactic search performed by standard GP with local search strategies for solution tuning, which is a simple strategy that has comparatively received much less attention. This work combines a recently proposed bloat-free GP called neat-GP with a local search strategy. One benefit of using a bloat-free GP is that it reduces the size of the parameter space confronted by the local searcher, offsetting some of the added computational cost. The algorithm is validated on a real-world problem with promising results.

References

  1. M. Castelli, L. Trujillo, L. Vanneschi, and A. Popovič. Prediction of energy performance of residential buildings: A genetic programming approach. Energy and Buildings, 102:67--74, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  2. X. Chen, Y.-S. Ong, M.-H. Lim, and K. C. Tan. A multi-facet survey on memetic computation. Evolutionary Computation, IEEE Transactions on, 15(5):591--607, Oct 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Dignum and R. Poli. Genetic Programming: 11th European Conference, EuroGP 2008, chapter Operator Equalisation and Bloat Free GP, pages 110--121. Springer Berlin Heidelberg, Berlin, Heidelberg, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. García-Valdez, L. Trujillo, J.-J. Merelo, F. Fernández de Vega, and G. Olague. The evospace model for pool-based evolutionary algorithms. Journal of Grid Computing, 13(3):329--349, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Kommenda, G. Kronberger, S. M. Winkler, M. Affenzeller, and S. Wagner. Effects of constant optimization by nonlinear least squares minimization in symbolic regression. In Genetic and Evolutionary Computation Conference, GECCO '13, Amsterdam, The Netherlands, July 6--10, 2013, Companion Material Proceedings, pages 1121--1128, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. R. Koza. Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines, 11(3--4):251--284, Sept. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. P. R. Langdon W.B. Foundations of Genetic Programming. Springer-Verlag Berlin Heidelberg, 2002.Google ScholarGoogle Scholar
  9. A. Moraglio, K. Krawiec, and C. G. Johnson. Geometric semantic genetic programming. In Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I, PPSN'12, pages 21--31, Berlin, Heidelberg, 2012. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Silva. Reassembling operator equalisation: A secret revealed. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO '11, pages 1395--1402, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. Silva and E. Costa. Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines, 10(2):141--179, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2):99--127, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. L. Trujillo, P. Legrand, G. Olague, and J. LéVy-VéHel. Evolving estimators of the pointwise hölder exponent with genetic programming. Inf. Sci., 209:61--79, Nov. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. L. Trujillo, L. Muñoz, E. Galván-López, and S. Silva. neat genetic programming: Controlling bloat naturally. Information Sciences, 333:21 -- 43, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. E. Z-Flores, L. Trujillo, O. Schütze, and P. Legrand. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, chapter Evaluating the Effects of Local Search in Genetic Programming, pages 213--228. Springer International Publishing, Cham, 2014.Google ScholarGoogle Scholar
  16. E. Z-Flores, L. Trujillo, O. Schütze, and P. Legrand. A local search approach to genetic programming for binary classification. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO '15, pages 1151--1158, New York, NY, USA, 2015. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Conferences
        GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion
        July 2016
        1510 pages
        ISBN:9781450343237
        DOI:10.1145/2908961

        Copyright © 2016 ACM

        Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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        Association for Computing Machinery

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        Publication History

        • Published: 20 July 2016

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        GECCO '16 Companion Paper Acceptance Rate137of381submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

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