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Incorporating sub-programs as knowledge in program synthesis by PushGP and adaptive replacement mutation

Authors:

Yifan He,

Claus Aranha,

Tetsuya SakuraiAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 554 - 557

https://doi.org/10.1145/3520304.3528891

Published: 19 July 2022 Publication History

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Abstract

Program synthesis aims to build an intelligent agent that composes computer programs to solve problems. Genetic programming (GP) provides an evolutionary solution for the program synthesis task. A typical GP includes a random initialization, an unguided variation, and a fitness-guided selection to search for a solution program. However, several recent studies have shown the importance of using prior knowledge in different components of the GP. This study investigates the effectiveness of incorporating sub-programs as "prior knowledge" into the variation process of GP by Replacement Mutation. We further design an adaptive strategy that allows the automatic selection of the helpful sub-programs to the search process from an archive (including helpful and unhelpful ones). With handcrafted sub-program archives, we verify the effectiveness of the Adaptive Replacement Mutation method in success rate. We demonstrate the effectiveness of our approach with transferred archives on two composite problems.

References

[1]

Thomas Helmuth, Nicholas Freitag McPhee, and Lee Spector. 2018. Program synthesis using uniform mutation by addition and deletion. In Proceedings of the Genetic and Evolutionary Computation Conference. 1127--1134.

Digital Library

Google Scholar

[2]

Thomas Helmuth, Edward Pantridge, Grace Woolson, and Lee Spector. 2020. Genetic Source Sensitivity and Transfer Learning in Genetic Programming. In Artificial Life Conference Proceedings. MIT Press One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit ..., 303--311.

Google Scholar

[3]

Thomas Helmuth and Lee Spector. 2015. General program synthesis benchmark suite. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. 1039--1046.

Digital Library

Google Scholar

[4]

Erik Hemberg, Jonathan Kelly, and Una-May O'Reilly. 2019. On domain knowledge and novelty to improve program synthesis performance with grammatical evolution. In Proceedings of the Genetic and Evolutionary Computation Conference. 1039--1046.

Digital Library

Google Scholar

[5]

Jonathan Kelly, Erik Hemberg, and Una-May O'Reilly. 2019. Improving genetic programming with novel exploration-exploitation control. In European Conference on Genetic Programming. Springer, 64--80.

Digital Library

Google Scholar

[6]

John R Koza and John R Koza. 1992. Genetic programming: on the programming of computers by means of natural selection. Vol. 1. MIT press.

Digital Library

Google Scholar

[7]

Edward Pantridge and Lee Spector. 2017. PyshGP: PushGP in python. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1255--1262.

Digital Library

Google Scholar

[8]

Dominik Sobania and Franz Rothlauf. 2019. Teaching GP to program like a human software developer: using perplexity pressure to guide program synthesis approaches. In Proceedings of the Genetic and Evolutionary Computation Conference. 1065--1074.

Digital Library

Google Scholar

[9]

Jordan Wick, Erik Hemberg, and Una-May O'Reilly. 2021. Getting a Head Start on Program Synthesis with Genetic Programming. In Genetic Programming. 263--279.

Google Scholar

Cited By

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Fernandes MDe França FFrancesquini ESilva SPaquete L(2023)HOTGP - Higher-Order Typed Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590464(1091-1099)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590464
He YAranha CSakurai T(2022)Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022128(14-21)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022128

Index Terms

Incorporating sub-programs as knowledge in program synthesis by PushGP and adaptive replacement mutation
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Automatic programming
        Genetic programming

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Published In

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

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Association for Computing Machinery

New York, NY, United States

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Published: 19 July 2022

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Cited By

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Fernandes MDe França FFrancesquini ESilva SPaquete L(2023)HOTGP - Higher-Order Typed Genetic ProgrammingProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590464(1091-1099)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590464
He YAranha CSakurai T(2022)Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022128(14-21)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022128

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Recommendations

General Program Synthesis Benchmark Suite

The PSI Program Model Builder - synthesis of very high-level programs

Algorithmic program synthesis: introduction