Skip to main content
SpringerLink
Log in
Book cover

Machine Learning and Systems Engineering pp 595–606Cite as

Analysing Multiobjective Fitness Function with Finite State Automata

  • Chapter
  • First Online:

  • 2957 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 68))

Abstract

This research analyses and discusses the use of Multiobjective fitness function to evolve Finite State Automata. Such automata can describe system’s behavior mathematically in an efficient manner. However system’s behavior must highly depend on its input-output specifications. Genetic Programming is used, and the fitness function is built to guide the evolutionary process in two different cases. First case: Single point fitness function is used where the only focus is on the correctness of the evolved automata. Second case: multiobjective fitness function is used since every real-world problem involves simultaneous optimization of several incommensurable and often competing objectives. Multiobjective optimization is defined as a problem of finding a Finite State Automata which satisfies: parsimony, efficiency, and correctness. It has been presented that for large and complex problems it is necessary to divide them into sub problem(s) and simultaneously breed both sub-program(s) and a calling program.

Nada M.A. Al Salami (1971) Assistance Professor in Management Information System Department, of Al Zaytoonah University, Amman, Jordan. She interested in the theory of computer science and evolutionary algorithms

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. K.C. Tan, E.F. Khor, T.H. Lee, Muliobjective Evolutionary Algorithms and Applications (Springer-Verlag, London limited, 2005

    Google Scholar 

  2. E. Zitzler, K. Deb, L. Thiele, Comparison of multiobjective evolutionary algorithm: empirical result. Evol. Comput. 8(2), 173–195 (2000), (Massachustts Institute of Technology)

    Article  Google Scholar 

  3. E. Zitzler, Evolutionary algorithm for multiobjective optimization, Evolutionary Methods for Design, Optimization and Control (CIMNE, Barcelona, Spain, 200)

    Google Scholar 

  4. J.D. Schaffner, Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Unpublished Ph.D. Thesis, Vanderbilt University, Nashville, TN, 1984

    Google Scholar 

  5. J.D. Schaffner, Multiple objective optimization with vector evaluated genetic algorithm, in Proceeding of an International Conference on Genetic Algorithms and their Applications, sponsored by Texas Instruments and the U.S. Navy Center for Applied Research in Artiffic1 intelligence (NCARAI) pp. 93–100, 1985

    Google Scholar 

  6. C.M. Fonseca, P.J. Fleming, Am overview of evolutionary algorithms in multiobjective optimization. Evol. Comput. 3(1), 1–16 (1995)

    Article  Google Scholar 

  7. C.A.C. Coello, A comprehensive survey of evolutionary-based multiobjective optimization. Knowl. Inf. Syst. 1(3), 269–308, 1999

    Google Scholar 

  8. L. Smith, Chaos: Avery Short Introduction (Oxford University Press, UK, 2007

    Google Scholar 

  9. J.R. Koza, Genetic Programming: on the Programming of Computer by Means of Natural Selection (MIT Press, Cambridge, MA, 2004)

    Google Scholar 

  10. D.E. Golberg, Genetic Algorithm in Search, Optimization, and Machine Learning (Addison-Wesley, Boston, MA, 1989)

    Google Scholar 

  11. M. Mitchell, An Introduction to Genetic Algorithm (MIT Press, Cambridge, MA, 1996)

    Google Scholar 

  12. N.A. Salami, Evolutionary algorithm definition. AJEAS 2(4), 789–795 (2009)

    Google Scholar 

  13. J.P. Koza, Two ways of discovering the size and shape of a computer program to solve a problem, pp. 287–294,1998

    Google Scholar 

  14. A. Kent, J.G. Williams, C.M. Hall, Genetic programming. Encyclopedia of Computer Science and Technology (Marcel Dekker, New York, 1998), pp. 29–43

    Google Scholar 

  15. R. Poli, W.B. Langdon, N.F. McPhee, J.R. Koza, Genetic Programming: An Introductory Tutorial and a Survey of Techniques and pplications, Technical Report CES-475 ISSN: 1744-8050. essex.ac.uk/dces/research/publications/…/2007/ces475.pdf. Oct 2007

    Google Scholar 

  16. N.A. Salami, Genetic system generation, in Proceeding of the WCECS 2009, vol. I. (San Francisco USA, 20–22 Oct 2009), pp. 23–27 ISBN:978-988-17012-6-8

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Information Systems, Faculty of Economic and Business, Al Zaytoonah University of Jordan, 130, Amman, 11733, Jordan

    Nada M. A. Al Salami (Assistant Professor)

Authors
  1. Nada M. A. Al Salami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nada M. A. Al Salami .

Editor information

Editors and Affiliations

  1. , Unit 1, 1/F, International Association of Engineers, Hung To Road 37-39, Hong Kong, Hong Kong/PR China

    Sio-Iong Ao

  2. Inst.Computerlinguistik, Abt. Linguistische Datenverarbeitung, Universität Trier, Trier, 54286, Germany

    Burghard Rieger

  3. Dept. Chemical Engineering, California State University, Long Beach, 90840, California, USA

    Mahyar A. Amouzegar

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Salami, N.M.A.A. (2010). Analysing Multiobjective Fitness Function with Finite State Automata. In: Ao, SI., Rieger, B., Amouzegar, M. (eds) Machine Learning and Systems Engineering. Lecture Notes in Electrical Engineering, vol 68. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9419-3_46

Download citation

  • DOI: https://doi.org/10.1007/978-90-481-9419-3_46

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-9418-6

  • Online ISBN: 978-90-481-9419-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation