Journal of Heuristics

, Volume 4, Issue 2, pp 179–192 | Cite as

Crossing Over Genetic Algorithms: The Sugal Generalised GA

  • Andrew Hunter


Sugal is a major new public-domain software package designed to support experimentation with, and implementation of, Genetic Algorithms. Sugal includes a generalised Genetic Algorithm, which supports the major popular versions of the GA as special cases. Sugal also has integrated support for various datatypes, including real numbers, and features to make hybridisation simple. This paper discusses the Sugal GA, showing how recombining the features of the popular algorithms results in the creation of a number of useful hybrid algorithms.

genetic algorithms simulated annealing software package 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Baker, J.E. (1985). “Adaptive Selection Methods for Genetic Algorithms.” Proc. First Int. Conf. on Genetic Algorithms and Their Applications, pp. 101-111.Google Scholar
  2. Bohachevsky, I.O., M.E. Johnson, and L.S. Myron. (1986). “Generalized Simulated Annealing for Function Optimization,” Technometrics28(3), 209-217.Google Scholar
  3. Davis, L. (1991). Handbook of Genetic Algorithms. Van Nostrand Reinhold.Google Scholar
  4. DeJong, K.A. (1975). “An Analysis of the Behavior of a Class of Genetic Adaptive Systems.” Doctoral Dissertation, University of Michigan. Dissertation Abstracts International 36(10), 5140B.Google Scholar
  5. Fogel, L.J., A.J. Owens, and M.J. Walsh. (1966). Artificial Intelligence Through Simulated Evolution. New York: John Wiley.Google Scholar
  6. Fogel, D.B., L.J. Fogel, and V.W. Porto. (1990). “Evolving Neural Networks,” Biological Cybernetics63, 487-493.Google Scholar
  7. Glover, F. (1977). “Heuristics for Integer Programming Using Surrogate Constraints,” Decision Sciences8(1), 156-166.Google Scholar
  8. Glover, F. (1997). “A Template for Scatter Search and Path Relinking.” In J.K. Hao, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers (eds.), Lecture Notes in Computer Science, pp. 1-45.Google Scholar
  9. Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.Google Scholar
  10. Harp, S.A., T. Samad, and A. Guha. (1989). “Towards the Genetic Synthesis of Neural Networks.” Int. Conf. on Genetic Algorithms, pp. 360-369.Google Scholar
  11. Holland, J. (1975). Adaptation in Natural and Artificial Systems. MIT Press.Google Scholar
  12. Hunter. (1995). “The Sugal Genetic Algorithms Package.” Scholar
  13. Schwefel, H. (1981). Numerical Optimization of Computer Models. Chicester: John Wiley.Google Scholar
  14. Whitley, D. (1989). “The GENITOR Algorithm and Selective Pressure: Why Rank-Based Allocation of Reproductive Trials is Best.” Proc. Third Int. Conf. on Genetic Algorithms. Morgan Kauffman.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Andrew Hunter
    • 1
  1. 1.Department of Computing and Information SystemsUniversity of SunderlandTyne and WearEngland. E-mail

Personalised recommendations