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Locally geometric semantic crossover

Published: 07 July 2012 Publication History

Abstract

We propose Locally Geometric Crossover (LGX) for genetic programming. For a pair of homologous loci in the parent solutions, LGX finds a semantically intermediate procedure from a previously prepared library, and uses it as replacement code. The experiments involving six symbolic regression problems show significant increase in search performance when compared to standard subtree-swapping cross-over and other control methods. This suggests that semantically geometric manipulations on subprograms propagate to entire programs and improve their fitness.

References

[1]
A. Guttman. R-trees: A dynamic index structure for spatial searching. In Proc. ACM SIGMOD Conf., page 47, Boston, MA, June 1984. Reprinted in M. Stonebraker, Readings in Database Sys., Morgan Kaufmann, San Mateo, CA, 1988.
[2]
J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
[3]
K. Krawiec. Medial crossovers for genetic programming. In A. Moraglio, S. Silva, K. Krawiec, P. Machado, and C. Cotta, editors, Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012, volume 7244 of LNCS, pages 62--73, Malaga, Spain, 11--13 April 2012. Springer Verlag.
[4]
K. Krawiec and B. Wieloch. Automatic generation and exploitation of related problems in genetic programming. In IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, 18--23 July 2010. IEEE Press.
[5]
S. Luke. The ECJ Owner's Manual -- A User Manual for the ECJ Evolutionary Computation Library, zeroth edition, online version 0.2 edition, Oct. 2010.
[6]
A. Moraglio, K. Krawiec, and C. Johnson. Geometric semantic genetic programming. In C. Igel, P. K. Lehre, and C. Witt, editors, The 5th workshop on Theory of Randomized Search Heuristics, ThRaSH'2011, Copenhagen, Denmark, July 8--9 2011.
[7]
R. Poli and W. B. Langdon. Schema theory for genetic programming with one-point crossover and point mutation. Evolutionary Computation, 6(3):231--252, 1998.

Cited By

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  • (2020)Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure DeduplicationGenetic Programming Theory and Practice XVII10.1007/978-3-030-39958-0_5(79-99)Online publication date: 8-May-2020
  • (2017)Selecting crackling product based on sensory analysis by different statistical data approaches2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2017.8261639(1-6)Online publication date: Nov-2017
  • (2017)Semantic genetic operators based on a selection mechanism tailored for the root function2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2017.8261638(1-6)Online publication date: Nov-2017
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Published In

cover image ACM Conferences
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
July 2012
1586 pages
ISBN:9781450311786
DOI:10.1145/2330784

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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Author Tags

  1. genetic programming
  2. program semantics
  3. semantic crossover

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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA

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

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Cited By

View all
  • (2020)Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure DeduplicationGenetic Programming Theory and Practice XVII10.1007/978-3-030-39958-0_5(79-99)Online publication date: 8-May-2020
  • (2017)Selecting crackling product based on sensory analysis by different statistical data approaches2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2017.8261639(1-6)Online publication date: Nov-2017
  • (2017)Semantic genetic operators based on a selection mechanism tailored for the root function2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2017.8261638(1-6)Online publication date: Nov-2017
  • (2017)Geometric Semantic Crossover with an Angle-Aware Mating Scheme in Genetic Programming for Symbolic RegressionGenetic Programming10.1007/978-3-319-55696-3_15(229-245)Online publication date: 15-Mar-2017
  • (2016)EvoDAG: A semantic Genetic Programming Python library2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2016.7830633(1-6)Online publication date: Nov-2016
  • (2015)Memetic Genetic Programming based on orthogonal projections in the phenotype space2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)10.1109/ROPEC.2015.7395160(1-6)Online publication date: Nov-2015
  • (2015)Review and comparative analysis of geometric semantic crossoversGenetic Programming and Evolvable Machines10.1007/s10710-014-9239-816:3(351-386)Online publication date: 1-Sep-2015
  • (2013)Locally geometric semantic crossoverGenetic Programming and Evolvable Machines10.1007/s10710-012-9172-714:1(31-63)Online publication date: 1-Mar-2013
  • (2012)Quantitative analysis of locally geometric semantic crossoverProceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I10.1007/978-3-642-32937-1_40(397-406)Online publication date: 1-Sep-2012

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