Abstract
A method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs is presented. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System (AS rank). To assess the evolvability of the system we applied a modified version of the method introduced in [9] to measure rate of evolution. Our results show that such method effectively reveals how evolution proceeds under different parameter settings. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity.
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References
Altenberg, L.: The evolution of evolvability in genetic programming. In: Advances in Genetic Programming, pp. 47–74. MIT Press, Cambridge (1994)
Bullnheimer, B., Hartl, R.F., Strauß, C.: A new rank based version of the ant system - a computational study. Central European Journal for Operations Research and Economics 7, 25–38 (1997)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: European Conference on Artificial Life, pp. 134–142 (1991)
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization – artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 1, 28–39 (2006)
Ghoulbeigi, E., dos Santos, M.V.: Probabilistic developmental program evolution. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC 2010, pp. 1138–1142. ACM, New York (2010)
Hara, A., Watanabe, M., Takahama, T.: Cartesian ant programming. In: SMC, pp. 3161–3166 (2011)
Harding, S., Miller, J.F., Banzhaf, W.: Smcgp2: self modifying cartesian genetic programming in two dimensions. In: GECCO, pp. 1491–1498 (2011)
Holker, G., dos Santos, M.V.: Toward an estimation of distribution algorithm for the evolution of artificial neural networks. In: Proceedings of the Third C* Conference on Computer Science and Software Engineering, C3S2E 2010, pp. 17–22. ACM, New York (2010)
Hu, T., Banzhaf, W.: Neutrality and variability: two sides of evolvability in linear genetic programming. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 963–970. ACM, New York (2009)
Miller, J.F., Thomson, P.: Cartesian genetic programming (2000)
Roux, O., Fonlupt, C.: Ant programming: Or how to use ants for automatic programming. In: From Ant Colonies to Artificial Ants 2nd International Workshop on Ant Colony Optimization (2000)
Shapiro, J.L.: Diversity Loss in General Estimation of Distribution Algorithms. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 92–101. Springer, Heidelberg (2006)
Shilane, D., Martikainen, J., Dudoit, S., Ovaska, S.J.: A general framework for statistical performance comparison of evolutionary computation algorithms. Inf. Sci. 178(14), 2870–2879 (2008)
Shirakawa, S., Ogino, S., Nagao, T.: Dynamic ant programming for automatic construction of programs. IEEJ Transactions on Electrical and Electronic Engineering TEEE 3, 540–548 (2008)
Stützle, T., Hoos, H.H.: MAX-MIN Ant System (November 1999)
Woodward, J.R.: Complexity and cartesian genetic programming. In: Mirkin, B., Magoulas, G. (eds.) The 5th Annual UK Workshop on Computational Intelligence, London, September 5-7, pp. 273–280 (2005)
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Luis, S., dos Santos, M.V. (2013). On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_10
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DOI: https://doi.org/10.1007/978-3-642-37207-0_10
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