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Comparing Genetic Programming Approaches for Non-functional Genetic Improvement

Blot, A; Petke, J; (2020) Comparing Genetic Programming Approaches for Non-functional Genetic Improvement. In: Hu, T and Lourenço, N and Medvet, E and Divina, F, (eds.) Genetic Programming. (pp. pp. 68-83). Springer Nature: Cham, Switzerland. Green open access

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Abstract

Genetic improvement (GI) uses automated search to find improved versions of existing software. While most GI work use genetic programming (GP) as the underlying search process, focus is usually given to the target software only. As a result, specifics of GP algorithms for GI are not well understood and rarely compared to one another. In this work, we propose a robust experimental protocol to compare different GI search processes and investigate several variants of GP- and random-based approaches. Through repeated experiments, we report a comparative analysis of these approaches, using one of the previously used GI scenarios: improvement of runtime of the MiniSAT satisfiability solver. We conclude that the test suites used have the most significant impact on the GI results. Both random and GP-based approaches are able to find improved software, even though the percentage of viable software variants is significantly smaller in the random case ( 14.5% vs. 80.1%). We also report that GI produces MiniSAT variants up to twice as fast as the original on sets of previously unseen instances from the same application domain.

Type: Proceedings paper
Title: Comparing Genetic Programming Approaches for Non-functional Genetic Improvement
Event: 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020
Location: Seville, Spain
Dates: 15th-17th April 2020
ISBN-13: 978-3-030-44093-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-44094-7_5
Publisher version: https://doi.org/10.1007/978-3-030-44094-7_5
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Genetic improvement (GI), Genetic programming (GP), Search-based software engineering (SBSE), Boolean satisfiability (SAT)
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10097404
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