research-article

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

Authors:

Vladimir Mironovich,

Maxim Buzdalov,

Valeriy VyatkinAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1902 - 1905

https://doi.org/10.1145/3205651.3208230

Published: 06 July 2018 Publication History

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GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

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

Pages 1902 - 1905

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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.

References

[1]

2012. International Standard IEC 61499-1: Function Blocks - Part 1: Architecture, 2nd ed. International Electrotechnical Commission, Geneva.

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Christel Baier, Joost-Pieter Katoen, and Kim Guldstrand Larsen. 2008. Principles of model checking. MIT press.

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G. Behrmann, A. David, K. G. Larsen, J. Hakansson, P. Petterson, Wang Yi, and M. Hendriks. 2006. UPPAAL 4.0. In Third International Conference on the Quantitative Evaluation of Systems - (QEST'06). 125--126.

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Mark Harman, S. Afsin Mansouri, and Yuanyuan Zhang. 2009. Search Based Software Engineering: A Comprehensive Analysis and Review of Trends, Technologies and Applications. Technical Report TR-09-03. Department of Computer Science, King's College London.

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Joseph Kempka, Phil McMinn, and Dirk Sudholt. 2013. A theoretical runtime and empirical analysis of different alternating variable searches for search-based testing. In Proceedings of Genetic and Evolutionary Computation Conference. 1445--1452.

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William B. Langdon and Gabriela Ochoa. 2016. Genetic improvement: A key challenge for evolutionary computation. In Proceedings of Congress on Evolutionary Computation.

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William B. Langdon, Nadarajen Veerapen, and Gabriela Ochoa. 2017. Visualising the Search Landscape of the Triangle Program. In European Conference on Genetic Programming. Number 10196 in Lecture Notes in Computer Science. 96--113.

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[8]

Vladimir Mironovich, Maxim Buzdalov, and Valeriy Vyatkin. 2017. Automatic generation of function block applications using evolutionary algorithms: Initial explorations. In Industrial Informatics (INDIN), 2017 IEEE 15th International Conference on. IEEE, 700--705.

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Mironovich VBuzdalov MVyatkin V(2021)Evaluation of Permutation-Based Mutation Operators on the Problem of Automatic Connection Matching in Closed-Loop Control SystemRecent Advances in Soft Computing and Cybernetics10.1007/978-3-030-61659-5_4(41-51)Online publication date: 6-Feb-2021
https://doi.org/10.1007/978-3-030-61659-5_4
Mironovich VBuzdalov MVyatkin V(2019)Permutation Encoding for Automatic Reconstruction of Connections in Closed-Loop Control System using Evolutionary Algorithm2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)10.1109/ETFA.2019.8869114(1265-1268)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1109/ETFA.2019.8869114

Index Terms

From fitness landscape analysis to designing evolutionary algorithms: the case study in automatic generation of function block applications
1. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

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 ACM 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

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Published: 06 July 2018

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Government of Russian Federation

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SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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

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Mironovich VBuzdalov MVyatkin V(2021)Evaluation of Permutation-Based Mutation Operators on the Problem of Automatic Connection Matching in Closed-Loop Control SystemRecent Advances in Soft Computing and Cybernetics10.1007/978-3-030-61659-5_4(41-51)Online publication date: 6-Feb-2021
https://doi.org/10.1007/978-3-030-61659-5_4
Mironovich VBuzdalov MVyatkin V(2019)Permutation Encoding for Automatic Reconstruction of Connections in Closed-Loop Control System using Evolutionary Algorithm2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)10.1109/ETFA.2019.8869114(1265-1268)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1109/ETFA.2019.8869114

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Diversity Guided Evolutionary Programming: A novel approach for continuous optimization

Analysis of evolutionary multi-tasking as an island model

Archived elitism in evolutionary computation: towards improving solution quality and population diversity

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