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Approximating geometric crossover by semantic backpropagation

Published: 06 July 2013 Publication History

Abstract

We propose a novel crossover operator for tree-based genetic programming, that produces approximately geometric offspring. We empirically analyze certain aspects of geometry of crossover operators and verify performance of the new operator on both, training and test fitness cases coming from set of symbolic regression benchmarks. The operator shows superior performance and higher probability of producing geometric offspring than tree-swapping crossover and other semantic-aware control methods.

References

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L. Beadle and C. Johnson. Semantically driven crossover in genetic programming. In J. Wang, editor, Proceedings of the IEEE World Congress on Computational Intelligence, pages 111{116, Hong Kong, 1-6 June 2008. IEEE Computational Intelligence Society, IEEE Press.
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K. Krawiec and P. Lichocki. Approximating geometric crossover in semantic space. In G. Raidl, et al., editors, GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 987{994, Montreal, 8-12 July 2009. ACM.
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K. Krawiec and T. Pawlak. Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators. Genetic Programming and Evolvable Machines, 14(1):31--63, 2013.
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J. McDermott, D. R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, W. Jaskowski, K. Krawiec, R. Harper, K. De Jong, and U.-M. O'Reilly. Genetic programming needs better benchmarks. In T. Soule, et al., editors, GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, pages 791--798, Philadelphia, Pennsylvania, USA, 7-11 July 2012. ACM.
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A. Moraglio, K. Krawiec, and C. G. Johnson. Geometric semantic genetic programming. In C. A. Coello Coello, et al., editors, Parallel Problem Solving from Nature, PPSN XII (part 1), volume 7491 of Lecture Notes in Computer Science, pages 21--31, Taormina, Italy, Sept. 1-5 2012. Springer.
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B. Wieloch and K. Krawiec. Running programs backwards: Instruction Inversion for Effective Search in Semantic Spaces. In Christian Blum, et al., editor, GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation, Amsterdam, The Netherlands, 2013. ACM.

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  • (2025)Memetic semantic boosting for symbolic regressionGenetic Programming and Evolvable Machines10.1007/s10710-024-09506-126:1Online publication date: 12-Feb-2025
  • (2024)A neural-guided dynamic symbolic network for exploring mathematical expressions from dataProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693202(28222-28242)Online publication date: 21-Jul-2024
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cover image ACM Conferences
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
July 2013
1672 pages
ISBN:9781450319638
DOI:10.1145/2463372
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 July 2013

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Author Tags

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

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  • Research-article

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GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2025)CaMo: Capturing the modularity by end-to-end models for Symbolic RegressionKnowledge-Based Systems10.1016/j.knosys.2024.112747309(112747)Online publication date: Jan-2025
  • (2025)Memetic semantic boosting for symbolic regressionGenetic Programming and Evolvable Machines10.1007/s10710-024-09506-126:1Online publication date: 12-Feb-2025
  • (2024)A neural-guided dynamic symbolic network for exploring mathematical expressions from dataProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693202(28222-28242)Online publication date: 21-Jul-2024
  • (2024)SymFormer: End-to-End Symbolic Regression Using Transformer-Based ArchitectureIEEE Access10.1109/ACCESS.2024.337464912(37840-37849)Online publication date: 2024
  • (2024)Interpretable scientific discovery with symbolic regression: a reviewArtificial Intelligence Review10.1007/s10462-023-10622-057:1Online publication date: 2-Jan-2024
  • (2024)Symbol Graph Genetic Programming for Symbolic RegressionParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_14(221-237)Online publication date: 7-Sep-2024
  • (2023)Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and AdaptabilityProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3595830(21-22)Online publication date: 15-Jul-2023
  • (2023)A geometric semantic macro-crossover operator for evolutionary feature construction in regressionGenetic Programming and Evolvable Machines10.1007/s10710-023-09465-z25:1Online publication date: 8-Dec-2023
  • (2023)Memetic Semantic Genetic Programming for Symbolic RegressionGenetic Programming10.1007/978-3-031-29573-7_13(198-212)Online publication date: 29-Mar-2023
  • (2022)Compositional genetic programming for symbolic regressionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3529077(570-573)Online publication date: 9-Jul-2022
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