Métodos Inteligentes de Otimização

Ementa:

Redes Neurais Artificiais: histórico. Aprendizagem supervisionada e não-supervisionada. Topologia em camadas, direta e realimentada. Algoritmos de aprendizagem. Diferentes topologias. Aplicações de redes neurais artificiais. Computação Evolucionária: histórico. Algoritmo canônico. Seleção. Cruzamento. Mutação. Algoritmos genéticos. Programação genética. Algoritmos evolucionários. Aplicações de computação evolucionária. Busca Tabu. Recozimento Simulado (simulated annealing).

Carga Horária:

45h/a.

Créditos:

3.

Bibliografia:

  1. Redes Neurais Artificiais:

    • GOLDEN, R. M.. Mathematical Methods for Neural Network Analysis and Design. The MIT Press, 1996, 419pp.
    • HAYKIN, Simon. Neural Networks - A Comprehensive Foundation, 2nd Edition. Prentice-Hall, 1999, 842pp.
    • HASSOUN, Mohamad H.. Fundamentals of Artificial Neural Networks. The MIT Press, 1995, 511pp.
    • MEHROTRA, Kishan, MOHAN, Chilukuri K., RANKA, Sanjay. Elements of Artificial Neural Networks. The MIT Press, 1997, 344pp.
    • GALLANT, Stephen I.. Neural Network Learning and Expert Systems. The MIT Press, 1994, 365pp.
    • SUNDARARAJAN, N., SARATCHANDRAN, P., WEI, Lu Ying. Radial Basis Function Neural Networks with Sequential Learning - MRAN and Its Application. World Scientific Publishing, 1999, 214pp.
    • FAUSETT, Laurene. Fundamentals of Neural Networks - Architectures, Algorithms, and Applications. Prentice-Hall, 1994, 461pp.
    • BISHOP, Christopher M.. Neural Networks for Pattern Recognition. Clarendon Press, 1995, 442pp.
    • VONK, E., JAIN, L. C., JOHNSON, R. P.. Automatic Generation of Neural Network Architecture Using Evolutionary Computation. World Scientific Publishing, 1997, 182pp.
    • SWINGLER, Kevin. Applying Neural Networks - A Practical Guide. Academic Press, 1996, 303pp.
    • SCHÖLKOPF, Bernhard, BURGES, Christopher J. C., SMOLA, Alexander J.. Advances in Kernel Methods - Support Vector Learning. The MIT Press, 1999, 376pp.
    • CLOETE, Ian, ZURADA, Jacek M.. Knowledge-Based Neurocomputing. The MIT Press, 2000, 486pp.
    • BRAGA, Antônio de Pádua, LUDERMIR, Teresa Bernarda, CARVALHO, André Carlos Ponce de Leon Ferreira. Redes Neurais Artificiais - Teoria e Aplicações. LTC Editora, 2000, 262pp.
    • REED, Russel D., MARKS II, Robert J.. Neural Smithing - Supervised Learning in Feedforward Artificial Neural Networks. The MIT Press, 1999, 346pp.
    • HINTON, Geoffrey, SEJNOWSKI, Terrence J.. Unsupervised Learning - Foundations of Neural Computation. The MIT Press, 1999, 398pp.
    • PRINCIPE, José C., EULIANO, Neil R.LEFEBVRE, W. Curt. Neural and Adaptive Systems - Fundamentals Through Simulations. John Willey & Sons, Inc., 2000, 656pp.

  2. Computação Evolucionária:

    • MITCHELL, Melanie. An Introduction to Genetic Algorithms. ISBN 0-262-63185-7, MIT Press, Cambridge, MA, 1996..
    • GOLDBERG, David E.. Genetic Algorithms in Search, Optimization and Machine Learning. ISBN 0-201-15767-5, 1989.
    • KOZA, J. R.. Genetic Programming. The MIT Press, Cambridge, Massachusetts, 1992.
    • FOGEL, D. B.. Evolutionary computation: Toward a new philosophy of machine intelligence. ISBN 0-7803-5379-X, 2nd Ed.,1995.
    • HOLLAND, J. H.. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, Michigan, 1975.
    • BÄCK, T.. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, New York, 1996.
    • MICHALEWICZ, Z.. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, third edition, 1996.
    • VOSE, M. D.. The Simple Genetic Algorithm: Foundations and Theory. The MIT Press, Cambridge, MA, 1999.
    • KARABOGA, D., PHAM, D. T.. Intelligent Optimisation Techniques. Springer-Verlag, ISBN: 1852330287, 2000.