skip to main content
10.1145/2330784.2330883acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Test-based extended finite-state machines induction with evolutionary algorithms and ant colony optimization

Authors Info & Claims
Published:07 July 2012Publication History

ABSTRACT

In this paper we consider the problem of extended finite-state machines induction. The input data for this problem is a set of tests. Each test consists of two sequences - an input sequence and a corresponding output sequence. We present a new method of Extended Finite-State Machines (EFSM) induction based on an Ant Colony Optimization algorithm (ACO) and a new meaningful test-based crossover operator for EFSMs. New algorithms are compared with a genetic algorithm (GA) using a traditional crossover, a (1+1) evolutionary strategy and a random mutation hill climber. This comparison shows that the use of test-based crossover greatly improves performance of GA. GA on average also significantly outperforms the hill climber and evolutionary strategy. ACO outperforms GA, and the difference between average performance of ACO and GA hybridized with hill climber is insignificant.

References

  1. Clark J., et al. Reformulating Software Engineering as a Search Problem. IEEE Proceedings-Software, vol. 150, 3, 2003, pp. 161--175.Google ScholarGoogle Scholar
  2. Harman M., Mansouri A. and Zhang Y. Search-Based Software Engineering: A Comprehensive Analysis and Review of Trends, Techniques, and Applications, tech. report TR- 09-03, Dept. of Computer Science, King's College London, 2009.Google ScholarGoogle Scholar
  3. Harman M. Software Engineering Meets Evolutionary Computation // Computer. 2011. Vol. 44, 11, pp. 31 -- 39. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Polikarpova, N. and Shalyto, A. 2009. Automata-based programming. Piter (in Russian).Google ScholarGoogle Scholar
  5. Dorigo, M. 1992. Optimization, Learning and Natural Algorithms. PhD thesis. Polytechnico di Milano, Italy.Google ScholarGoogle Scholar
  6. Lucas, S. and Reynolds, J. Learning DFA: Evolution versus Evidence Driven State Merging. The 2003 Congress on Evolutionary Computation (CEC'03). Vol. 1, 351--348.Google ScholarGoogle Scholar
  7. Lucas, S. Evolving Finite-State Transducers: Some Initial Explorations. Lecture Notes in Computer Science. Springer Berlin / Heidelberg. Volume 2610/2003, pp. 241--257.Google ScholarGoogle Scholar
  8. Hamming, R. Error detecting and error correcting codes. Bell System Technical Journal 29 (2), pp. 147--160.Google ScholarGoogle ScholarCross RefCross Ref
  9. Levenshtein, V. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10: 707--10. 1966.Google ScholarGoogle Scholar
  10. Johnson, C. Genetic Programming with Fitness based on Model Checking. Lecture Notes in Computer Science. Springer Berlin / Heidelberg, 2007. Volume 4445/2007, pp. 114--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Lucas, S. and Reynolds, J. Learning Deterministic Finite Automata with a Smart State Labeling Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 27, 7, 2005, pp. 1063--1074. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Zaikin, A. Development of finite-state machines induction methods with simulated annealing for the "Competition for resources" game. Nauchno-technicheskij vestnik SPbGU ITMO (in Russian). 2011. 2, pp. 49 -- 54.Google ScholarGoogle Scholar
  13. Tsarev, F. 2010. Method of finite-state machine induction from tests with genetic programming. Information and Control Systems (Informatsionno-upravljayushiye sistemy, in Russian), no. 5, pp. 31--36. http://is.ifmo.ru/works/_zarev.pdfGoogle ScholarGoogle Scholar
  14. Tsarev, F. and Egorov, K. Finite-state machine induction using genetic algorithm based on testing and model checking. 2011. In Proceedings of the 2011 GECCO Conference Companion on Genetic and Evolutionary Computation (GECCO'11). NY. ACM. 2011. pp. 759--762. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Alba, E. and Chicano, F. 2007. ACOhg: dealing with huge graphs. In Proceedings of the 9th annual conference on Genetic and evolutionary computing (GECCO'07). ACM, NY, USA, pp. 10--17. Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
    July 2012
    1586 pages
    ISBN:9781450311786
    DOI:10.1145/2330784

    Copyright © 2012 ACM

    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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 7 July 2012

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article

    Acceptance Rates

    Overall Acceptance Rate1,669of4,410submissions,38%

    Upcoming Conference

    GECCO '24
    Genetic and Evolutionary Computation Conference
    July 14 - 18, 2024
    Melbourne , VIC , Australia

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader