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BIBLIOGRAFÍA BÁSICA


1. Metaheuristics: From design to Implementation. El-Ghazali Talbi, ISBN: 978-0-470-27858-1, (2009).

2. An introduction to Genetic Algorithm. M. Mitchell, ISBN-10: 0-262-63185-7, (1998)

3. How learning can guide evolution. Complex Systems 1.Geoffrey E. Hinton and Steven J. Nowlan, Ed. Witley, pp. 495-502 (1987).

4. Cooperation and community structure in artificial ecosystems. Kristian Lindgren and Mats G. Nordahl, Ed. MITPress, Vol 1, no. 1-2, pp. 15-37, (1994).

5. Aprendizaje automático. D. Borrajo, J. González y P. Isasi, Sanz y Torres, (2006).

6. Genetic algorithms in search, optimization, and machine learning. David E. Goldberg, Addison-Wesley, 1989.

7. An introduction to Genetic Algorithms, Tutorial online de Marek Obitko (URL)

8. How learning can guide evolution. (URL)

BIBLIOGRAFÍA RECOMENDADA


9. Evolutionary algorithms in theory and practice : evolution strategies, evolutionary programming, genetic algorithms. Bäck, Thomas, Oxford University Press. 1996.

10. Genetic programming : on the programming of computers by means of natural selection. John R. Koza, MIT Press. 1992.

11. Genetic algorithms + data structures = evolution programs. Michalewicz, Zbigniew, Springer, 1992.

12. Foundations of genetic algorithms. Rawlins, Gregory J.E., Morgan Kaufman, 1991

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