Abstract
Genetic Programming (GP), an evolutionary method, can be used to solve difficult problems in various applications. However, three important problems in GP are its tendency to find non-parsimonious solutions (bloat), to converge prematurely and to use a tremendous amount of computing time. In this paper, we present an efficient model of distributed GP to limit these general GP drawbacks. This model uses a multi-objective optimization and a hierarchical communication topology.
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Frey, J., Gras, R., Hernandez, P., Appel, R. (2004). A Hierarchical Model of Parallel Genetic Programming Applied to Bioinformatic Problems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_147
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DOI: https://doi.org/10.1007/978-3-540-24669-5_147
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21946-0
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