Abstract
We show genetic programming (GP) populations can evolve under the influence of a Pareto multi-objective fitness and program size selection scheme, from “perfect.” programs which match the training material to general solutions. The technique is demonstrated with programmatic image compression, two machine learning benchmark problems (Pima Diabetes and Wisconsin Breast Cancer) and an insurance customer profiling task (Benelearn99 data mining).
Cite
CITATION STYLE
Langdon, W. B., & Nordin, J. P. (2000). Seeding genetic programming populations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1802, pp. 304–315). Springer Verlag. https://doi.org/10.1007/978-3-540-46239-2_23
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