Abstract
We propose a Grammatical Evolution approach to the automatic design of Ant Colony Optimization algorithms. The grammar adopted by this framework has the ability to guide the learning of novel architectures, by rearranging components regularly found on human designed variants. Results obtained with several TSP instances show that the evolved algorithmic strategies are effective, exhibit a good generalization capability and are competitive with human designed variants.
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Tavares, J., Pereira, F.B. (2012). Automatic Design of Ant Algorithms with Grammatical Evolution. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_18
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DOI: https://doi.org/10.1007/978-3-642-29139-5_18
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