Semantic fitness function in genetic programming based on semantics flow analysis

Pak-kan WONG, Man-leung WONG, Kwong-sak LEUNG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

The search performance of conventional Genetic Programming (GP) methods is strongly guided by the performance of the fitness function. In each generation, the fitness function evaluates every program in the population and measures the distance between the final output of the programs and the desired output. Human programmers often rely on the feedback from the intermediate execution states, which are the semantics, to localize and resolve software bugs. However, the semantics of a program is seldom explicitly considered in the fitness function to assess the quality of a program in GP. In this paper, we invent methods to improve fitness evaluation leveraging semantics in GP. We propose semantics flow analysis for programs using information theoretic concepts. Next, we develop a novel semantic fitness evaluation technique to rank programs using semantics based on the semantics flow analysis. Our evaluation results show that adopting our method can improve the success rates in Grammar-Based GP.

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
EditorsManuel López-Ibáñez
PublisherAssociation for Computing Machinery (ACM)
Pages354-355
Number of pages2
ISBN (Electronic)9781450367486
ISBN (Print)9781450367486
DOIs
Publication statusPublished - 13 Jul 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Publication series

NameGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Abbreviated titleGECCO 19
CountryCzech Republic
CityPrague
Period13/07/1917/07/19

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Genetic programming
Semantics
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Bibliographical note

This research is supported by Institute of Future Cities of The Chinese University of Hong Kong.

Keywords

  • Genetic Programming
  • Semantics
  • Fitness
  • Semantics Flow

Cite this

WONG, P., WONG, M., & LEUNG, K. (2019). Semantic fitness function in genetic programming based on semantics flow analysis. In M. López-Ibáñez (Ed.), GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 354-355). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery (ACM). https://doi.org/10.1145/3319619.3321960
WONG, Pak-kan ; WONG, Man-leung ; LEUNG, Kwong-sak. / Semantic fitness function in genetic programming based on semantics flow analysis. GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. editor / Manuel López-Ibáñez . Association for Computing Machinery (ACM), 2019. pp. 354-355 (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion).
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abstract = "The search performance of conventional Genetic Programming (GP) methods is strongly guided by the performance of the fitness function. In each generation, the fitness function evaluates every program in the population and measures the distance between the final output of the programs and the desired output. Human programmers often rely on the feedback from the intermediate execution states, which are the semantics, to localize and resolve software bugs. However, the semantics of a program is seldom explicitly considered in the fitness function to assess the quality of a program in GP. In this paper, we invent methods to improve fitness evaluation leveraging semantics in GP. We propose semantics flow analysis for programs using information theoretic concepts. Next, we develop a novel semantic fitness evaluation technique to rank programs using semantics based on the semantics flow analysis. Our evaluation results show that adopting our method can improve the success rates in Grammar-Based GP.",
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WONG, P, WONG, M & LEUNG, K 2019, Semantic fitness function in genetic programming based on semantics flow analysis. in M López-Ibáñez (ed.), GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery (ACM), pp. 354-355, 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 13/07/19. https://doi.org/10.1145/3319619.3321960

Semantic fitness function in genetic programming based on semantics flow analysis. / WONG, Pak-kan; WONG, Man-leung; LEUNG, Kwong-sak.

GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. ed. / Manuel López-Ibáñez . Association for Computing Machinery (ACM), 2019. p. 354-355 (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion).

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

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WONG P, WONG M, LEUNG K. Semantic fitness function in genetic programming based on semantics flow analysis. In López-Ibáñez M, editor, GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). 2019. p. 354-355. (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). https://doi.org/10.1145/3319619.3321960