Semantic fitness function in genetic programming based on semantics flow analysis

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

doi:10.1145/3319619.3321960

Semantic fitness function in genetic programming based on semantics flow analysis

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

Lingnan University

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-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 language	English
Title of host publication	GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
Editors	Manuel López-Ibáñez
Publisher	Association for Computing Machinery (ACM)
Pages	354-355
Number of pages	2
ISBN (Electronic)	9781450367486
ISBN (Print)	9781450367486
DOIs	https://doi.org/10.1145/3319619.3321960
Publication status	Published - 13 Jul 2019
Event	2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic Duration: 13 Jul 2019 → 17 Jul 2019

Publication series

Name	GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference	2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Abbreviated title	GECCO 19
Country/Territory	Czech Republic
City	Prague
Period	13/07/19 → 17/07/19

Funding

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

Keywords

Genetic Programming
Semantics
Fitness
Semantics Flow

Access to Document

10.1145/3319619.3321960

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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title = "Semantic fitness function in genetic programming based on semantics flow analysis",

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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author = "Pak-kan WONG and Man-leung WONG and Kwong-sak LEUNG",

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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 Proceedings › Conference paper (refereed) › Research › peer-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). doi: 10.1145/3319619.3321960

Semantic fitness function in genetic programming based on semantics flow analysis

Abstract

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Keywords

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