Titlebook: Genetic Programming; 8th European Confere Maarten Keijzer,Andrea Tettamanzi,Marco Tomassini Conference proceedings 2005 Springer-Verlag Ber

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https://doi.org/10.1007/978-3-8349-3818-3 version of GP and the second one is to employ populations that can change size dynamically and adaptively. The latter approach consists in deleting or adding individuals in the population as a function of the current fitness and two other parameters. We test this approach on three well-known proble

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https://doi.org/10.1007/978-3-658-41844-1ed substructures like alpha helices and beta sheets. This suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary approach is used as the inference procedure for folded structures on simple lattice models. The algorithm

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https://doi.org/10.1007/978-3-8349-9392-2Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also

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https://doi.org/10.1007/978-3-531-19789-0nation with genetic search in a tree adjoining grammar guided genetic programming system (TAG3P). The results show that, on the problems tried, duplication/truncation works well as a mutation operator but not reliably when the complexity of the problem was scaled up. When using these dual operators

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https://doi.org/10.1007/978-3-8349-9773-9erein robotic tanks fight to destruction in a closed arena. Using genetic programming to evolve tank strategies for this highly active forum, we were able to rank third out of twenty-seven players in the category of HaikuBots. Our GPBot was the only entry not written by a human.