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Issue:Generalized net for evaluation of the genetic algorithm fitness function

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Title of paper: Generalized net for evaluation of the genetic algorithm fitness fuction
Author(s):
Olympia Roeva
Centre for Biomedical Engineering — Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, Sofia-1113, BULGARIA
olympia@clbme.bas.bg
Krassimir Atanassov
Centre for Biomedical Engineering — Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 105, Sofia-1113, BULGARIA
krat@bas.bg
Anthony Shannon
KvB Institute of Technology, North Sydney, 2060, AUSTRALIA
Warrane College, University of New South Wales, Kensington, 1465, AUSTRALIA
   (current: t.shannon@warrane.unsw.edu.au)
Presented at: 8th IWGN, Sofia, 26 June 2007
Published in: Conference proceedings, pages 48—55
Download: Download-icon.png PDF (120  Kb, Info)
Abstract: Using the apparatus of Generalized nets (GN) a GN model of a genetic algorithm is developed. The presented GN model describes the genetic algorithm search procedure based on the mechanism of natural selection. The GN model simultaneously evaluates several fitness functions, ranks the individuals according to their fitness and has the opportunity to choice the best fitness function regarding to specific problem domain.
Keywords: Generalized nets, Genetic algorithms, Fitness function
References:
  1. Aladjov, H., K. Atanassov. A generalized net for genetic algorithms learning. — In: Proc. of the XXX Spring Conf. of the Union of Bulgarian Mathematicians, Borovets, 2001, 242{249.
  2. Atanassov, K., Generalized Nets, World Scientific, Singapore, New Jersey, London 1991.
  3. Atanassov, K., H. Aladjov. Generalized Nets in Artificial Intelligence. Vol. 2: Generalized nets and Machine Learning. "Prof. M. Drinov" Academic Publishing House, Sofia, 2000.
  4. Chen Q., K. Worden, P. Peng, A.Y.T. Leung, Genetic algorithm with an improved fitness function for (N)ARX modelling, Mechanical Systems and Signal Processing, 21(2), 2007, 994{1007.
  5. Clerc F., R. Rakotomalala, D. Farrusseng, Learning fitness function in a combinatorial optimization process, - In: Proc. of International Symposium on Applied Stochastic Models and Data Analysis (ASMDA), May, 17-20, 2005, 535—542.
  6. Diaz-Gomez P. A., D. F. Hougen, Genetic Algorithms for Hunting Snakes in Hypercubes: Fitness Function Analysis and Open Questions, - In: Proc. of Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06), 2006, 389-394.
  7. Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Massachusetts, 1989.
  8. Inagaki J., M. Haseyama, H. Kitajima, A New Fitness Function of a Genetic Algorithm for Routing Applications, IEICE transactions on information and systems, E84-D(2), 2001, 277-280.
  9. Kazarlis S., V. Petridis, Varying fitness functions in genetic algorithms: Studying the rate of increase of the dynamic penalty terms, Lecture Notes in Computer Science, 1498/1998, 2006, 211-220.
  10. Kita H., Y. Sano, Genetic Algorithms for Optimization of Noisy Fitness Functions and Adaptation to Changing Environments, 2003 Joint Workshop of Hayashibara Foundation and SMAPIP, July 11-15, 2003, Okayama, Japan.
  11. Man, K., K. Tang, S. Kwong. Genetic Algorithms. Concepts and Designs. London, Springer-Verlag, 1999.
  12. Mitchell M., An Introduction to Genetic Algorithms (Complex Adaptive Systems Series). Cambridge, MA: MIT Press, 1996.
  13. Pohlheim, H.: Genetic and Evolutionary Algorithms: Principles, Methods and Algorithms, available at http://www.systemtechnik.tu-lmenau.de/~pohlheim/GA_Toolbox/algindex.html
  14. Vise, M. The Simple Genetic Algorithm. Cambridge, MIT Press, 1999.
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