Abstract
In this work a new approach, named Bayesian Automatic Programming (BAP), to inducing programs is presented. BAP integrates the power of grammar evolution and probabilistic models to evolve programs. We explore the use of BAP in two domains: a regression problem and the artificial ant problem. Its results are compared with traditional Genetic Programming (GP). The experimental results found encourage further investigation, especially to explore BAP in other domains and to improve the proposed approach to incorporating new mechanisms.
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Regolin, E.N., Pozo, A.T.R. (2005). Bayesian Automatic Programming. In: Keijzer, M., Tettamanzi, A., Collet, P., van Hemert, J., Tomassini, M. (eds) Genetic Programming. EuroGP 2005. Lecture Notes in Computer Science, vol 3447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31989-4_4
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DOI: https://doi.org/10.1007/978-3-540-31989-4_4
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