Abstract
This chapter captures the experience acquired in the development of applications based on genetic algorithms. Specifically, we implemented two games that show an intelligent behaviour by executing genetic algorithms. They both show good results as well, because they are able to play successfully against human players. Moreover, the genetic algorithms parameters are user-configurable; so, the user can modify the number of individuals per generation, the number of generations, the mutation probability of each individual, the crossover function to generate new individuals, etc. This is very useful because the applications developed also generate statistical reports that show how individuals evolve in each generation. Therefore, the user can understand the evolution and analyze results easily. With this approach the user can test several combinations of parameters to study and compare them by analyzing their behaviour, speed, etc. In conclusion, as we are going to see in this chapter, the implementation of these two genetic games is an interesting strategy in order to teach and learn genetic algorithms.
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Aznar, F.; Suau, P.; Compañ, P.; Rizo, R. Aprender Jugando: ¿Qué Opinan los Alumnos?. XII JENUI, Bilbao, Spain, pp. 199-206, July 2006. (in Spanish)
Cordova, D.I.; Lepper, M.R. Intrinsic Motivation and the Process of Learning: Beneficial Effects of Contextualization, Personalization, and Choice. Journal of Educational Psychology, vol. 88, no. 4, pp. 715-730, 1996.
Gallego, F.; Satorre, R.; Llorens, F. Computer Games Tell, Show, Involve, and Teach. 8th Int. Symposium on Computers in Education, León, Spain, pp. 157-165, October 2006.
Garris, R.; Ahlers, R.; Driskell, J.E. Games, Motivation and Learning: A Research and Practice Model. Simulation & Gaming, vol. 33, no. 4, pp. 441-467, 2002.
Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, January 1989.
Granado-Criado, J.M.; Vega-Rodrìguez, M.A.; Ballesteros-Rubio, J.; Sánchez-Pérez, J.M.; Gómez-Pulido, J.A. Teaching Reconfigurable Computing in the New EHEA by means of the Multicycle MIPS Machine Implementation. 6th International Workshop on Microelectronics Education, Stockholm, Sweden, pp. 104-107, June 2006.
Lepper, M.R.; Cordova, D.I. A Desire to be Taught: Instructional Consequences of Intrinsic Motivation. Motivation and Emotion, vol. 16, pp. 187-208, 1992.
Martens, R.L.; Gulikers, J.; Bastiaens, T. The Impact of Intrinsic Motivation on E-Learning in Authentic Computer Tasks. Journal of Computer Assisted Learning, vol. 20, no. 5, pp. 368-376, 2004.
Mitchell, M. An Introduction to Genetic Algorithms. The MIT Press, February 1998.
Overmars, M. Teaching Computer Science through Game Design. IEEE Computer, vol. 37, no. 4, pp. 81-83, 2004.
Paredes-Juárez, R.G.; Ramírez-Morales, A.A.; Saito-Vázquez N. A. http://www.cs.tcu.edu/ people/professors/asanchez/cosc40503/RasGA/index.html, 2006.
Vega-Rodríguez, M.A.; Sánchez-Pérez, J.M.; Gómez-Pulido, J.A. An Educational Tool for Testing Caches on Symmetric Multiprocessors. Microprocessors and Microsystems, vol. 25, no. 4, pp. 187-194, June 2001.
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Chaves-González, J.M., Otero-Mateo, N., Vega-Rodríguez, M.A., Sánchez-Pérez, J.M., Gómez-Pulido, J.A. (2007). Game Implementation: An Interesting Strategy to Teach Genetic Algorithms. In: Fernández-Manjón, B., Sánchez-Pérez, J.M., Gómez-Pulido, J.A., Vega-Rodríguez, M.A., Bravo-Rodríguez, J. (eds) Computers and Education. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-4914-9_18
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