Soft Computing

, Volume 9, Issue 8, pp 594–605 | Cite as

Adaptive representation for single objective optimization

  • Crina Grosan
  • Mihai Oltean


A new technique called Adaptive Representation Evolutionary Algorithm (AREA) is proposed in this paper. AREA involves dynamic alphabets for encoding solutions. The proposed adaptive representation is more compact than binary representation. Genetic operators are usually more aggressive when higher alphabets are used. Therefore the proposed encoding ensures an efficient exploration of the search space. This technique may be used for single and multiobjective optimization. We treat the case of single objective optimization problems in this paper. Despite its simplicity the AREA method is able to generate a population converging towards optimal solutions. Numerical experiments indicate that the AREA technique performs better than other single objective evolutionary algorithms on the considered test functions.


Evolution strategy Single objective optimization Adaptive representation Higher alphabets encoding 


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  1. Angeline P (1995) Two self-adaptive crossover operators for genetic programming. In: Angeline PJ, Kinnear KE.Jr, (Eds.) Advances in genetic programming, 2. MIT Press, Cambridge, MA 89–109Google Scholar
  2. Bäck T (1993) Optimal mutation rate in genetic search. In: Forest S (Ed.) Proceedings of the 5th international conference in genetic algorithms. Morgan Kaufmann, San Mateo, CA 2–8Google Scholar
  3. Bäck T, Schütz M (1996) Intelligent mutation rate control in canonical genetic algorithms. In: Ras ZW, Michalewicz M (Eds.) Foundations on Intelligent Systems. Lectures Notes in Artificial Intelligence. Springer, Berlin Heidelber New York, Vol. 1079 pp. 158–167Google Scholar
  4. Booker LB (1987) Improving search in Genetic Algorithms. In: Davis L (Ed.) Genetic algorithms and simulated annealing. Morgan Kaufmann, San Mateo, CA 61–73Google Scholar
  5. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evolutionary Computation 124–133Google Scholar
  6. Elseth GD, Baumgardner KD (1995) Principles of Modern Genetics, West Publishing CompanyGoogle Scholar
  7. Goldberg DE (1989) Genetic algorithms in search, Optimization and machine learning. Addison-Wesley, New YorkGoogle Scholar
  8. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  9. Kimura M (1983) The neutral theory of molecular evolution. Cambridge University PressGoogle Scholar
  10. Kingdon J, Dekker L (1995) The shape of space. Technical Report RN/95/23, Intelligent System Lab, Department of Computer Science, University College London, LondonGoogle Scholar
  11. Rechenberg I (1973) Evolutions strategie: Optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog Verlag, StuttgartGoogle Scholar
  12. Schwefel HP (1981) Numerical optimization of computer models. John Wiley, ChichesterGoogle Scholar
  13. Schaffer JD, Morishima A (1987) An adaptive crossover distribution mechanism for genetic algorithms. Grefenstette JJ (Ed.) In: Proceedings of the 2nd international conference on genetic algorithms. Lawrence Erlbaum Associates, Hillsdale, NJ 3640Google Scholar
  14. Shaefer CG (1987) The ARGOT Strategy: Adaptive representation genetic optimizer technique, in Laurence, Erlbaum (Eds.) ICGA II. Hillsdale, NJGoogle Scholar
  15. Spears WM (1992) Adapting crossover in a genetic algorithm. Report AIC92025, Navy Center for applied research in artificial intelligence, USAGoogle Scholar
  16. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization, IEEE Trans Evolutionary Computation, 1:67–82Google Scholar
  17. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolutionary Computation, Vol. 3(2):82–102Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Crina Grosan
    • 1
  • Mihai Oltean
    • 1
  1. 1.Department of Computer Science, Faculty of Mathematics and Computer ScienceBabes-Bolyai UniversityCluj-NapocaRomania

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