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Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming

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Genetic Programming Theory and Practice XVIII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

Search-Based Software Engineering problems frequently have semantic constraints that can be used to deterministically restrict what type of programs can be generated, improving the performance of Genetic Programming. Strongly-Typed and Grammar-Guided Genetic Programming are two examples of using domain-knowledge to improve performance of Genetic Programming by preventing solutions that are known to be invalid from ever being added to the population. However, the restrictions in real world challenges like program synthesis, automated program repair or test generation are more complex than what context-free grammars or simple types can express. We address these limitations with examples, and discuss the process of efficiently generating individuals in the context of Christiansen Grammatical Evolution and Refined-Typed Genetic Programming. We present three new approaches for the population initialization procedure of semantically constrained GP that are more efficient and promote more diversity than traditional Grammatical Evolution.

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Notes

  1. 1.

    We use the term ‘probability’ loosely, not with a statistical meaning.

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Acknowledgements

This work was supported by the Fundação para a Ciência e a Tecnologia (FCT) under LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020), the GADgET project (DSAIPA/DS/0022/2018) and the CMU Portugal project CAMELOT (POCI-01-0247-FEDER-045915).

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Authors and Affiliations

  1. LASIGE, Departamento de Informática da Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal

    Alcides Fonseca, Paulo Santos, Guilherme Espada & Sara Silva

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  1. Alcides Fonseca
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  2. Paulo Santos
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  3. Guilherme Espada
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  4. Sara Silva
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Correspondence to Alcides Fonseca .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Tecnológico Nacional de México/IT de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  4. Evolution Enterprise, Ann Arbor, MI, USA

    Bill Worzel

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Fonseca, A., Santos, P., Espada, G., Silva, S. (2022). Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming. In: Banzhaf, W., Trujillo, L., Winkler, S., Worzel, B. (eds) Genetic Programming Theory and Practice XVIII. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-16-8113-4_3

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