Navegando por Palavras-chave "Evolutionary algorithms"

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    Algoritmos evolutivos e modelo HP para predição de estruturas de proteínas
    (Sociedade Brasileira de Automática, 2012-02-01) Gabriel, Paulo H. R.; Melo, Vinicius Veloso de [UNIFESP]; Delbem, Alexandre C. B.; Universidade de São Paulo (USP); Universidade Federal de São Paulo (UNIFESP)
    Protein structures prediction (PSP) is a computationally complex problem. Simplified models of the protein molecule (such as the HP Model) and the use of evolutionary algorithms (EAs) are among the most investigated techniques for PSP. However, the evaluation of a structure represented by the HP model considers only the number of hydrophobic contacts, which doesn't enable the EA to distinguish between structures with the same number of contacts. This paper presents a new multi-objective formulation for PSP in HP Model. Two metrics are evaluated: the number of hydrophobic contacts and the distance between the hydrophobic amino acids. Both metrics are used by the Multi-objective EA in Tables. We showed that the algorithm is fast and robust.
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    Evolutionary model trees for handling continuous classes in machine learning
    (Elsevier B.V., 2011-03-01) Barros, Rodrigo C.; Ruiz, Duncan D.; Basgalupp, Marcio P. [UNIFESP]; Universidade de São Paulo (USP); Pontificia Univ Catolica Rio Grande do Sul; Universidade Federal de São Paulo (UNIFESP)
    Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. in this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.