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Lecture type	Total
Lectures	30

* Load is given in academic hour (1 academic hour = 45 minutes)

Fuzzy, evolutionary and neuro-computation is a group of methods that differ from classical computational methods in very fundamental ideas. They are based on approximate reasoning, self learning, parallelism and non-determinism. Inspiration for these methods comes from biology e.g. biological neuron, process of evolution, human like approximate reasoning etc. These methods can solve problems that cannot be solved by applying classical mathematical and computational methods and they are used in scientific research and in myriad applications and everyday products.

1. Genetic Algorithms + Data Structures = Evolution Programs; Z.Michalewicz; Springer Verlag, Berlin, 3rd ed.; 1996; ISBN: 3642082335
2. Neural Networks, Comprehensive Foundation; S.Haykin; Prentice Hall, 2nd ed.; 1999; ISBN: 0132733501
3. Fuzzy Logic; J. Yen and R. Langari; Prentice Hall; 1999
4. Fuzzy Set Theory and Its Applications; H.J.Zimmermann; Kluwer Academic Publishers, 4th ed.; 2001; ISBN: 9780792374350
5. Practical Genetic Algorithms; R. L. Haupt, S. E. Haupt; Wiley-Interscience; 2 Ed.; 2004; ISBN: 9780471455653
6. Handbook of Genetic Algorithms;L. Davis;1991;Van Nostrand Reinhold, New York

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