Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31903
Intuition Operator: Providing Genomes with Reason

Authors: Grigorios N. Beligiannis, Georgios A. Tsirogiannis, Panayotis E. Pintelas


In this contribution, the use of a new genetic operator is proposed. The main advantage of using this operator is that it is able to assist the evolution procedure to converge faster towards the optimal solution of a problem. This new genetic operator is called ''intuition'' operator. Generally speaking, one can claim that this operator is a way to include any heuristic or any other local knowledge, concerning the problem, that cannot be embedded in the fitness function. Simulation results show that the use of this operator increases significantly the performance of the classic Genetic Algorithm by increasing the convergence speed of its population.

Keywords: Genetic algorithms, intuition operator, reasonable genomes, complex search space, nonlinear fitness functions

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1428


[1] T. Bäck, D. B. Fogel and Z. Michaelewicz, Eds., Handbook of Evolutionary Computation, Institute of Physics Publishing Ltd and Oxford University Press, 1997.
[2] W. Banzhaf, C. Reeves and C. R. Reeves, Eds., Foundations of Genetic Algorithms 1999 (FOGA 5) (Foundations of Genetic Algorithms),Morgan Kaufmann, 1999.
[3] L. Chambers, Ed., Practical Handbook of Genetic Algorithms: Applications: Volume I, Boca Raton, FL: CRC Press, 1995.
[4] L. Chambers, Ed., Practical Handbook of Genetic Algorithms: New Frontiers. Volume II, Boca Raton, FL: CRC Press, 1995.
[5] L. Chambers, Ed., Practical Handbook of Genetic Algorithms: Complex Coding Systems: Volume III, Boca Raton, FL: CRC Press, 1999.
[6] D. A. Coley, An introduction to Genetic Algorithms for Scientists and Engineers, Singapore: World Scientific, 1999.
[7] L. Davis, Ed., Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991.
[8] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Reading, Mass.: Addison-Wesley, 1989.
[9] R. Haupt, S. E. Haupt, Practical Genetic Algorithms, John Wiley & Sons Inc, 1998.
[10] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 3rd ed., N. Y.: Springer-Verlag, 1996.
[11] K. F. Man, K. S. Tang, S. Kwong and W. A. Halang, Genetic Algorithms for Control and Signal Processing, London: Springer-Verlag, 1997.
[12] K. F. Man, K. S. Tang, S. Kwong and W. A. Halang, Genetic Algorithms: Concepts and Designs, London: Springer-Verlag, 1999.
[13] M. Mitchell, An Introduction to Genetic Algorithms (Complex Adaptive Systems), Cambridge, Massachusetts, London, England: A Bradford Book, The MIT Press, 1998.
[14] M. Vose, The Simple Genetic Algorithm: Foundations and Theory,Cambridge, MA: The MIT Press, 1999.
[15] K. De Jong, ''An analysis of the behaviour of a class of genetic adaptive systems'', PhD thesis, University of Michigan, 1975.
[16] E. W. Weisstein, ''Method of Steepest Descent'', MathWorld - A Wolfram Web Resource. Available:
[17] GAlib - A C++ Library of Genetic Algorithm Components, Matthew Wall, Massachusetts Institute of Technology (MIT). Available: