Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
Intelligent Optimisation Techniques

Abstract

This chapter consists of six main sections. The first four sections briefly introduce the basic principles of genetic algorithms, tabu search, simulated annealing and neural networks. To give an indication of the relative performances of these techniques, the last two sections present the results obtained using them to optimise a set of numeric test functions and a travelling salesman problem.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Aarts, E. and Korst, J. (1989) Simulated Annealing and Boltzmann Machines, Wiley, Chichester, UK.

    MATH  Google Scholar 

  • Back, T., Hoffmeister, F. and Schwefel, H.P. (1991) A survey of evolution strategies, Proc. 4 th Int. Conf. on Genetic Algorithms and their Applications, Morgan Kaufmann, San Mateo, CA, pp.2–9.

    Google Scholar 

  • Baker, J.E. (1985) Adaptive selection methods for genetic algorithms, Proc. 1 st Int. Conf. on Genetic Alorithms and their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, pp.101–111.

    Google Scholar 

  • Carpenter, G. A. and Grossberg, S. (1988) The ART of Adaptive Pattern Recognition by a Self-Organising Neural Network, Computer, March, pp.77–88.

    Google Scholar 

  • Carster, P. and Bo, S. (1989) A new method for mapping optimization problems onto neural networks, Int. J. of Neural Systems, Vol.1, No.1, pp.3–22.

    Article  Google Scholar 

  • Carster, P. (1990) Parallel distributed approaches to combinatorial optimization: benchmark studies on traveling salesman problem, Neural Computation, Vol.2, pp.261–269.

    Article  Google Scholar 

  • Cichocki, A., and Unbehauen, R. (1993) Neural Network for Optimization Signal Processing, Wiley, Chichester, UK.

    Google Scholar 

  • Davis, L. (1991) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, NY.

    Google Scholar 

  • De Jong, K.A. (1975) Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. Dissertation, Department of Computer and Communication Science, University of Michigan, Ann Arbor, MI.

    Google Scholar 

  • Elman, J. L. (1990) Finding structure in time, Cognitive Science, Vol.14, pp.179–211.

    Article  Google Scholar 

  • Fogarty, T.C. (1989) Varying the probability of mutation in the genetic algorithm, Proc. 3 rd Int. Conf. on Genetic Algorithms and Their Applications, George Mason University, pp.104–109.

    Google Scholar 

  • Glover, F. (1986) Future paths for integer programming and links to artificial intelligence, Computers and Operation Research, Vol.13, pp.533–549.

    Article  MathSciNet  MATH  Google Scholar 

  • Glover, F. (1989) Tabu Search-part I, ORSA Journal on Computing, Vol.1., pp.190–206.

    MathSciNet  MATH  Google Scholar 

  • Glover, F. (1990) Tabu Search-part II, ORSA Journal on Computing, Vol.2., pp.14–32.

    Google Scholar 

  • Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Grefenstette, J. J. (1986) Optimisation of control parameters for genetic algorithms, IEEE Trans. on Systems, Man and Cybernetics, Vol.SMC-16, No.1, pp.122–128.

    Article  Google Scholar 

  • Hansen, P. (1986) The steepest ascent mildest descent heuristic for combinatorial programming, Conf. on Numerical Methods in Combinatorial Optimisation, Capri, Italy.

    Google Scholar 

  • Hecht-Nielsen, R. (1990) Neurocomputing, Addison-Wesley, Reading, MA.

    Google Scholar 

  • Holland, J. H. (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.

    Google Scholar 

  • Hopfield, J. J. (1982) Neural networks and physical systems with emergent collective computational abilities, Proc. National Academy of Sciences, USA, April, Vol.79, pp.2554–2558.

    Google Scholar 

  • Hopfield, J. J., and Tank, D. W. (1985) “Neural” computation of decisions in optimization problems, Biol. Cybern., Vol.52, pp.141–152.

    MathSciNet  MATH  Google Scholar 

  • Jordan, M. I. (1986) Attractor dynamics and parallelism in a connectionist sequential machine, Proc. 8 th Annual Conf. of the Cognitive Science Society, Amherst, MA, pp.531–546.

    Google Scholar 

  • Kirkpatrick, S., Gelatt, C.D. Jr and Vecchi, M.P. (1983) Optimization by simulated annealing, Science, Vol.220, No.4598, pp.671–680.

    Article  MathSciNet  Google Scholar 

  • Kohonen, T. (1989) Self-Organisation and Associative Memory (3rd ed.), Springer-Verlag, Berlin.

    Google Scholar 

  • Martina, G. S. (1989) ASPARAGOS An asynchronous parallel genetic optimization strategy, Proc. 3 rd Int. ICGA Conf., pp.422–427.

    Google Scholar 

  • Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953) Equation of state calculations by fast computing machines, J. of Chem. Phys., Vol.21, No.6, pp.1087–1092.

    Article  Google Scholar 

  • Michalewicz, Z. (1992) Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, New York, NY.

    MATH  Google Scholar 

  • Muhlenbein, H., Gorges-Schleuter, M. and Kramar, O. (1988) Evolution algorithms in combinatorial optimization, Parallel Computing, Vol.7, pp.65–85.

    Article  Google Scholar 

  • Osman, I.H. (1991) Metastrategy Simulated Annealing and Tabu Search Algorithms for Combinatorial Optimisation Problems, PhD Thesis, University of London, Imperial College, UK.

    Google Scholar 

  • Reeves, C.R. (1995) Modern Heuristic Techniques for Combinatorial Problems, McGraw-Hill, Maidenhead, UK.

    Google Scholar 

  • Rumelhart, D. E. and J. L. McClelland (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, MA.

    Google Scholar 

  • Schaffer, J.D., Caruana, R.A., Eshelman, L.J. and Das, R. (1989) A study of control parameters affecting on-line performance of genetic algorithms for function optimisation, Proc. 3 rd Int. Conf. on Genetic Algorithms and their Applications, George Mason University, pp.51–61.

    Google Scholar 

  • Wasserman, P.D. (1989) Neural Computing, Van Nostrand Reinhold, New York, NY.

    Google Scholar 

  • Whitely, D. and Hanson, T. (1989) Optiming neural networks using faster, more accurate genetic search, Proc. 3 rd Int. Conf. on Genetic Algorithms and their Applications, George Mason University, pp.370–374.

    Google Scholar 

  • Widrow, B. and M. E. Hoff (1960) Adaptive switching circuits, Proc. IRE WESCON Convention Record, Part 4, IRE, New York, pp.96–104.

    Google Scholar 

  • Zurada, J. M. (1992) Introduction to Artificial Neural Systems, West, Eagan, MN.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Systems Division, School of Engineering, University of Wales, Cardiff, PO Box 688, Newport Road, Cardiff, CF2 3TE, UK

    D. T. Pham BE, PhD, DEng

  2. Department of Electronic Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey

    D. Karaboga Bsc MSc, PhD

Authors
  1. D. T. Pham BE, PhD, DEng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Karaboga Bsc MSc, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag London Limited

About this chapter

Cite this chapter

Pham, D.T., Karaboga, D. (2000). Introduction. In: Intelligent Optimisation Techniques. Springer, London. https://doi.org/10.1007/978-1-4471-0721-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-0721-7_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-1186-3

  • Online ISBN: 978-1-4471-0721-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation