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.
Cite
CITATION STYLE
Frey, J., Gras, R., Hernandez, P., & Appel, R. (2004). A hierarchical model of parallel genetic programming applied to bioinformatic problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3019, pp. 1146–1153). Springer Verlag. https://doi.org/10.1007/978-3-540-24669-5_147
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