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Counterexample-driven genetic programming without formal specifications

Authors:

Thomas Helmuth,

Lee Spector,

Edward PantridgeAuthors Info & Claims

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

Pages 239 - 240

https://doi.org/10.1145/3377929.3389983

Published: 08 July 2020 Publication History

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Abstract

Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints in order to generate the training cases used to evaluate the evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called "informal CDGP," to software synthesis problems.

References

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Cited By

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Helmuth TPantridge EFrazier JSpector L(2024)Generational Computation Reduction in Informal Counterexample-Driven Genetic ProgrammingGenetic Programming10.1007/978-3-031-56957-9_2(21-37)Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-56957-9_2
Helmuth TFrazier JShi YAbdelrehim ASilva SPaquete L(2023)Human-Driven Genetic Programming for Program Synthesis: A PrototypeProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596373(1981-1989)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596373
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

Index Terms

Counterexample-driven genetic programming without formal specifications
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming

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Abstract
Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints to generate the training cases used to evaluate evolving programs. It has also been extended to combine formal constraints and user-provided ...

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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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Helmuth TPantridge EFrazier JSpector L(2024)Generational Computation Reduction in Informal Counterexample-Driven Genetic ProgrammingGenetic Programming10.1007/978-3-031-56957-9_2(21-37)Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1007/978-3-031-56957-9_2
Helmuth TFrazier JShi YAbdelrehim ASilva SPaquete L(2023)Human-Driven Genetic Programming for Program Synthesis: A PrototypeProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596373(1981-1989)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3596373
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

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