Abstract
Genetic Programming (GP) is one of the Evolutionary Algorithms. There are many theories concerning automatic code generation. In this article we present the latest research of using our dynamic scaling parameter in Genetic Programming to create a code. We have created practically functioning program code with the dynamic instruction set for L language. For testing we have chosen the best known problems. Our investigations of the best range of each parameter were based on our preliminary experiments.
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Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming - An Introduction, pp. 133–134. Morgan Kaufmann Publishers (1998)
Łysek, T., Boryczka, M.: Dynamic parameters in GP and LGP. In: Nguyen, N.T., Trawiński, B., Katarzyniak, R., Jo, G.-S. (eds.) Adv. Methods for Comput. Collective Intelligence. SCI, vol. 457, pp. 219–228. Springer, Heidelberg (2013)
Brameier, M., Banzhaf, W.: Linear Genetic Programming, pp. 130, 183–185, 186. Springer (2007)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)
Luke, S., Spector, L.: A Comparison of Crossover and Mutation in Genetic Programming (1997)
Łysek T.: Dedicated language and MVC platform for Genetic Programming algorithms. Journal of Information, Control and Managament Systems (2012)
Nedjah, N., Abraham, A., de Macedo Mourelle, L.: Genetic Systems Programming: Theory and Experiences, pp. 16–17. Springer-Verlag (2006)
Weise, T.: Global Optimization Algorithms: Theory and Application, pp. 169–174, 191–195, 207–208 (2009)
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Łysek, T., Boryczka, M. (2014). Genetic Programming with Dynamically Regulated Parameters for Generating Program Code. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_37
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DOI: https://doi.org/10.1007/978-3-319-11289-3_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11288-6
Online ISBN: 978-3-319-11289-3
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