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Transfer Learning of Genetic Programming Instruction Sets

Authors:

Thomas Helmuth,

Edward Pantridge,

Grace Woolson,

Lee SpectorAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 241 - 242

https://doi.org/10.1145/3377929.3389988

Published: 08 July 2020 Publication History

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Abstract

The performance of a genetic programming system depends partially on the composition of the collection of elements out of which programs can be constructed, and by the relative probability of different instructions and constants being chosen for inclusion in randomly generated programs or for introduction by mutation. In this paper we develop a method for the transfer learning of instruction sets across different software synthesis problems. These instruction sets outperform unlearned instruction sets on a range of problems.

References

[1]

Thomas Helmuth and Lee Spector. 2015. General Program Synthesis Benchmark Suite. In GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. ACM, Madrid, Spain, 1039--1046.

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[2]

Erik Hemberg, Jonathan Kelly, and Una-May O'Reilly. 2019. On domain knowledge and novelty to improve program synthesis performance with grammatical evolution. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. ACM, Prague, Czech Republic, 1039--1046. https://doi.org/

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[3]

John R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA. http://mitpress.mit.edu/books/genetic-programming

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Luis Muñoz, Leonardo Trujillo, and Sara Silva. 2019. Transfer learning in constructive induction with Genetic Programming. Genetic Programming and Evolvable Machines (Nov. 2019).

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Lee Spector and Alan Robinson. 2002. Genetic Programming and Autoconstructive Evolution with the Push Programming Language. Genetic Programming and Evolvable Machines 3, 1 (March 2002), 7--40. https://doi.org/

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Cited By

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Trujillo LNation JMuñoz LGalván E(2023)Predicting the success of transfer learning for genetic programming using DeepInsight feature space alignmentAI Communications10.3233/AIC-23010436:3(159-173)Online publication date: 21-Aug-2023
https://doi.org/10.3233/AIC-230104
Sobania DSchweim DRothlauf F(2023)A Comprehensive Survey on Program Synthesis With Evolutionary AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.316232427:1(82-97)Online publication date: Feb-2023
https://doi.org/10.1109/TEVC.2022.3162324
Al-Helali BChen QXue BZhang M(2021)Multitree Genetic Programming With New Operators for Transfer Learning in Symbolic Regression With Incomplete DataIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.307984325:6(1049-1063)Online publication date: Dec-2021
https://doi.org/10.1109/TEVC.2021.3079843
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Index Terms

Transfer Learning of Genetic Programming Instruction Sets
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming

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Published In

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

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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Association for Computing Machinery

New York, NY, United States

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Published: 08 July 2020

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National Science Foundation

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GECCO '20

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SIGEVO

GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

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

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Trujillo LNation JMuñoz LGalván E(2023)Predicting the success of transfer learning for genetic programming using DeepInsight feature space alignmentAI Communications10.3233/AIC-23010436:3(159-173)Online publication date: 21-Aug-2023
https://doi.org/10.3233/AIC-230104
Sobania DSchweim DRothlauf F(2023)A Comprehensive Survey on Program Synthesis With Evolutionary AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.316232427:1(82-97)Online publication date: Feb-2023
https://doi.org/10.1109/TEVC.2022.3162324
Al-Helali BChen QXue BZhang M(2021)Multitree Genetic Programming With New Operators for Transfer Learning in Symbolic Regression With Incomplete DataIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.307984325:6(1049-1063)Online publication date: Dec-2021
https://doi.org/10.1109/TEVC.2021.3079843
Wick JHemberg EO’Reilly U(2021)Getting a Head Start on Program Synthesis with Genetic ProgrammingGenetic Programming10.1007/978-3-030-72812-0_17(263-279)Online publication date: 25-Mar-2021
https://doi.org/10.1007/978-3-030-72812-0_17

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Transfer learning: a building block selection mechanism in genetic programming for symbolic regression

Generating interlocked instruction pipelines from specifications of instruction sets

A Selection Hyper-Heuristic for Transfer Learning in Genetic Programming

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