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PSXO: population-wide semantic crossover

Published: 15 July 2017 Publication History

Abstract

Since its introduction, Geometric Semantic Genetic Programming (GSGP) has been the inspiration to ideas on how to reach optimal solutions efficiently. Among these, in 2016 Pawlak has shown how to analytically construct optimal programs by means of a linear combination of a set of random programs. Given the simplicity and excellent results of this method (LC) when compared to GSGP, the author concluded that GSGP is "overkill". However, LC has limitations, and it was tested only on simple benchmarks. In this paper, we introduce a new method, Population-Wide Semantic Crossover (PSXO), also based on linear combinations of random programs, that overcomes these limitations. We test the first variant (Inv) on a diverse set of complex real-life problems, comparing it to LC, GSGP and standard GP. We realize that, on the studied problems, both LC and Inv are outperformed by GSGP, and sometimes also by standard GP. This leads us to the conclusion that GSGP is not overkill. We also introduce a second variant (GPinv) that integrates evolution with the approximation of optimal programs by means of linear combinations. GPinv outperforms both LC and Inv on unseen test data for the studied problems.

References

[1]
Mauro Castelli, Leonardo Trujillo, Leonardo Vanneschi, Sara Silva, Emigdio Z-Flores, and Pierrick Legrand. 2015. Geometric Semantic Genetic Programming with Local Search. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO '15). ACM, New York, NY, USA, 999--1006.
[2]
John R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.
[3]
Alberto Moraglio, Krzysztof Krawiec, and ColinG. Johnson. 2012. Geometric Semantic Genetic Programming. In Parallel Problem Solving from Nature - PPSN XII, CarlosA.Coello Coello, Vincenzo Cutello, Kalyanmoy Deb, Stephanie Forrest, Giuseppe Nicosia, and Mario Pavone (Eds.). Lecture Notes in Computer Science, Vol. 7491. Springer Berlin Heidelberg, 21--31.
[4]
Tomasz P. Pawlak. 2016. Geometric Semantic Genetic Programming Is Overkill. In Genetic Programming: 19th European Conference, EuroGP 2016, Porto, Portugal, March 30 - April 1, 2016, Proceedings, Malcolm I. Heywood, James McDermott, Mauro Castelli, Ernesto Costa, and Kevin Sim (Eds.). Springer, 246--260.
[5]
Leonardo Vanneschi, Mauro Castelli, Luca Manzoni, and Sara Silva. 2013. A New Implementation of Geometric Semantic GP and its Application to Problems in Pharmacokinetics. In Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013 (LNCS), Vol. 7831. Springer Verlag, Vienna, Austria, 205--216.
[6]
Leonardo Vanneschi, Sara Silva, Mauro Castelli, and Luca Manzoni. 2014. Geometric semantic genetic programming for real life applications. In Genetic Programming Theory and Practice XI. Springer New York, 191--209.

Cited By

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  • (2020)Learning feature spaces for regression with genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-020-09383-4Online publication date: 11-Mar-2020
  • (2019)Semantic variation operators for multidimensional genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321776(1056-1064)Online publication date: 13-Jul-2019

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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2017
1934 pages
ISBN:9781450349390
DOI:10.1145/3067695
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 15 July 2017

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Author Tags

  1. inverse matrix
  2. population-wide crossover
  3. real-life problems
  4. semantics

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  • FCT/MCTES/PIDDAC, Portugal
  • National Science Centre, Poland

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GECCO '17
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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2020)Learning feature spaces for regression with genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-020-09383-4Online publication date: 11-Mar-2020
  • (2019)Semantic variation operators for multidimensional genetic programmingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321776(1056-1064)Online publication date: 13-Jul-2019

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