From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications

Vladimir Mironovich; Maxim Buzdalov; Valeriy Vyatkin

doi:10.1145/3205651.3208230

From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications

Vladimir Mironovich, Maxim Buzdalov, Valeriy Vyatkin

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

2 Citations (Scopus)

Abstract

Search-based software engineering, a discipline that often requires finding optimal solutions, can be a viable source for problems that bridge theory and practice of evolutionary computation. In this research we consider one such problem: generation of data connections in a distributed control application designed according to the IEC 61499 industry standard.

We perform the analysis of the fitness landscape of this problem and find why exactly the simplistic (1 + 1) evolutionary algorithm is slower than expected when finding an optimal solution to this problem. To counteract, we develop a population-based algorithm that explicitly maximises diversity among the individuals in the population. We show that this measure indeed helps to improve the running times.

Original language	English
Title of host publication	GECCO '18
Subtitle of host publication	Proceedings of the Genetic and Evolutionary Computation Conference Companion
Editors	Hernan Aguirre
Publisher	Association for Computing Machinery
Pages	1902-1905
Number of pages	4
ISBN (Print)	978-1-4503-5764-7
DOIs	https://doi.org/10.1145/3205651.3208230
Publication status	Published - 06 Jul 2018
Externally published	Yes
Event	GECCO 2018: The Genetic and Evolutionary Computation Conference - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018 http://gecco-2018.sigevo.org

Conference

Conference	GECCO 2018: The Genetic and Evolutionary Computation Conference
Country/Territory	Japan
City	Kyoto
Period	15 Jul 2018 → 19 Jul 2018
Internet address	http://gecco-2018.sigevo.org

Keywords

evolutionary computation
population diversity
program synthesis
search-based software engineering

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10.1145/3205651.3208230

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Mironovich, V., Buzdalov, M., & Vyatkin, V. (2018). From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications . In H. Aguirre (Ed.), GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1902-1905). Association for Computing Machinery. https://doi.org/10.1145/3205651.3208230

Mironovich, Vladimir ; Buzdalov, Maxim ; Vyatkin, Valeriy. / From fitness landscape analysis to designing evolutionary algorithms : the case study in automatic generation of function block applications . GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion. editor / Hernan Aguirre. Association for Computing Machinery, 2018. pp. 1902-1905

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Mironovich, V, Buzdalov, M & Vyatkin, V 2018, From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications . in H Aguirre (ed.), GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, pp. 1902-1905, GECCO 2018: The Genetic and Evolutionary Computation Conference, Kyoto, Japan, 15 Jul 2018. https://doi.org/10.1145/3205651.3208230

From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications . / Mironovich, Vladimir; Buzdalov, Maxim; Vyatkin, Valeriy.
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion. ed. / Hernan Aguirre. Association for Computing Machinery, 2018. p. 1902-1905.

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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Mironovich V, Buzdalov M, Vyatkin V. From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications . In Aguirre H, editor, GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery. 2018. p. 1902-1905 doi: 10.1145/3205651.3208230

From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications

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