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Semantic fitness function in genetic programming based on semantics flow analysis

Published: 13 July 2019 Publication History

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.

References

[1]
Thomas M Cover and Joy A Thomas. 2012. Elements of information theory. John Wiley & Sons.
[2]
John R Koza. 1992. Genetic Programming: vol. 1, On the programming of computers by means of natural selection. Vol. 1. MIT press.
[3]
Krzysztof Krawiec. 2016. Behavioral Program Synthesis with Genetic Programming. Vol. 618. Springer.
[4]
William B Langdon. 2002. How many Good Programs are there? How Long are they?. In Proceedings of the 2002 Foundations of Genetic Algorithms. 183--202.
[5]
Peter Alexander Whigham. 1995. Grammatically-based genetic programming. In Proceedings of the Workshop on Genetic Programming: From Theory to Real-world Applications, Vol. 16. 33--41.
[6]
Man Leung Wong and Kwong Sak Leung. 1995. Applying Logic Grammars to Induce Sub-Functions in Genetic Programming. In Proceedings of the 1995 IEEE Conference on Evolutionary Computation, Vol. 2. IEEE, 737--740.

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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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Author Tags

  1. fitness
  2. genetic programming
  3. semantics
  4. semantics flow

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  • Research-article

Funding Sources

  • Lingnan University Direct Grant

Conference

GECCO '19
Sponsor:
GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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