Article

Geometric semantic genetic programming

Authors:

Alberto Moraglio,

Krzysztof Krawiec,

Colin G. JohnsonAuthors Info & Claims

PPSN'12: Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I

Pages 21 - 31

https://doi.org/10.1007/978-3-642-32937-1_3

Published: 01 September 2012 Publication History

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Abstract

Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view on search operators and representations, we bring the semantic approach to its extreme consequences and introduce a novel form of GP --- Geometric Semantic GP (GSGP) --- that searches directly the space of the underlying semantics of the programs. This perspective provides new insights on the relation between program syntax and semantics, search operators and fitness landscape, and allows for principled formal design of semantic search operators for different classes of problems. We derive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators.

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Cited By

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Corus DOliveto P(2024)On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block MutationsACM Transactions on Evolutionary Learning and Optimization10.1145/36771244:4(1-33)Online publication date: 16-Jul-2024
https://dl.acm.org/doi/10.1145/3677124
Antonov KKalkreuth RYang KBäck TStein NKononova ALi XHandl J(2024)A Functional Analysis Approach to Symbolic RegressionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654079(859-867)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654079
Dick G(2024)An ensemble learning interpretation of geometric semantic genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-024-09482-625:1Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1007/s10710-024-09482-6
Show More Cited By

Geometric semantic genetic programming
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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Information

Published In

PPSN'12: Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I

September 2012

541 pages

ISBN:9783642329364

Editors:
Carlos Coello Coello
Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Departmento de Computation, Av. IPN No. 2508, Col. San Pedro Zacatenco, Mexico, D.F., Mexico
,
Vincenzo Cutello
Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, Catania, Italy
,
Kalyanmoy Deb
Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology, Kanpur, V.le A. Doria 6, Kanpur, India
,
Stephanie Forrest
Department of Computer Science, University of New, V.le A. Doria 6, Mexico, India
,
Giuseppe Nicosia
Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, Catania, Italy

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Springer-Verlag

Berlin, Heidelberg

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Published: 01 September 2012

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Corus DOliveto P(2024)On the Generalisation Performance of Geometric Semantic Genetic Programming for Boolean Functions: Learning Block MutationsACM Transactions on Evolutionary Learning and Optimization10.1145/36771244:4(1-33)Online publication date: 16-Jul-2024
https://dl.acm.org/doi/10.1145/3677124
Antonov KKalkreuth RYang KBäck TStein NKononova ALi XHandl J(2024)A Functional Analysis Approach to Symbolic RegressionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654079(859-867)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654079
Dick G(2024)An ensemble learning interpretation of geometric semantic genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-024-09482-625:1Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1007/s10710-024-09482-6
Bonin LRovito LDe Lorenzo AManzoni L(2024)Cellular geometric semantic genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-024-09480-825:1Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1007/s10710-024-09480-8
Song JLu QTian BZhang JLuo JWang Z(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: 14-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70055-2_14
Vanneschi L(2024)SLIM_GSGP: The Non-bloating Geometric Semantic Genetic ProgrammingGenetic Programming10.1007/978-3-031-56957-9_8(125-141)Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-56957-9_8
Feser JDillig ISolar-Lezama A(2023)Inductive Program Synthesis Guided by Observational Program SimilarityProceedings of the ACM on Programming Languages10.1145/36228307:OOPSLA2(912-940)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622830
Farinati DBakurov IVanneschi L(2023)A study of dynamic populations in geometric semantic genetic programmingInformation Sciences: an International Journal10.1016/j.ins.2023.119513648:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.ins.2023.119513
Langdon W(2023)Jaws 30Genetic Programming and Evolvable Machines10.1007/s10710-023-09467-x24:2Online publication date: 22-Nov-2023
https://dl.acm.org/doi/10.1007/s10710-023-09467-x
Zhang HChen QXue BBanzhaf WZhang M(2023)Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature ConstructionPRICAI 2023: Trends in Artificial Intelligence10.1007/978-981-99-7022-3_36(385-397)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1007/978-981-99-7022-3_36
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New geometric semantic operators in genetic programming: perpendicular crossover and random segment mutation

Parameter evaluation of geometric semantic genetic programming in pharmacokinetics

Self-tuning geometric semantic Genetic Programming

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