Advances of Soft Computing in Engineering
Suggested readings
Burczynski, T. et al., Optimization and defect identification using distributed evolutionary algorithms, Eng. Appl. Artif. Intell., 17(4): 337–344, 2004.
Burczynski T. and Osyczka A. (Eds). 2004, Evolutionary Methods in Mechanics, Dortrecht, Kluwer Academic Publ.
Ghaboussi J., Biologically inspired soft computing methods in structural mechanics and engineering, Intern. J. Struct. Eng. Mech., 11 (5): 485–502, 2001.
Reed R.D. and Marks II R.J., Neural Smithing: Supevised Learning in Feedforward Artificial Neural Networks, MIT Press, 1999.
Haftka R.T. and Gurdal, Z., 1992, Elements of Structural Optimization, KluwerAcademic Publ., 3rd Edi.
Goldberg, D., 1989, Genetic Algorithms in Search, Optimisation, and Machine Learning, Addison Wesley Longman, Inc.
Parmee I.C, 2001, Evolutionary and Adaptive Computing in Engineering Design, Springer, London.
Bäck, Th., 1996, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford Univ. Press.
Toropov, V.V. and Mahfouz, S.Y. Design optimization of structuralsteelwork using a genetic algorithm, FEM and a system of design rules, Eng. Compu., 18(3/4): 437–459, 2001.
Ashour, A.F. et al., Empirical modelling of shear strength of RC deep beams by genetic programming. Compu. & Stru., 81: 331–338, 2003.
Haykin, S., 1999, Neural Networks – A Comprehensible Foundations, Prentice-Hall, 2nd edition.
Bishop Ch.M., Neural Networks for Pattern Recognirtion, Clarendon Press, Oxford.