Abstract
This paper proposes a fine-grained parallelization of the Genetic Programming paradigm (GP) using the Evolvable Agent model (EvAg) The algorithm is decentralized in order to take full-advantage of a massively parallel Peer-to-Peer infrastructure. In this context, GP is particularly demanding due to its high requirements of computational power. To assess the viability of the approach, the EvAg model has been empirically analyzed in a simulated Peer-to-Peer environment where experiments were conducted on two well-known GP problems. Results show that the spatially structured nature of the algorithm is able to yield a good quality in the solutions. Additionally, parallelization improves times to solution by several orders of magnitude.
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Jiménez Laredo, J.L., Lombraña González, D., Fernández de Vega, F., García Arenas, M., Merelo Guervós, J.J. (2011). A Peer-to-Peer Approach to Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds) Genetic Programming. EuroGP 2011. Lecture Notes in Computer Science, vol 6621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20407-4_10
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