Abstract
A large number of practical optimization problems involve elements of quite diverse nature described as mixtures of qualitative and quantitative information and whose description is possibly incomplete. In this work we present an extension of the breeder genetic algorithm that represents and manipulates this heterogeneous information in a natural way. The algorithm is illustrated in a set of optimization tasks involving the training of different kinds of neural networks. An extensive experimental study is presented in order to show the potential of the algorithm.
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Muñoz, L.A.B. (2002). Evolutionary Optimization of Heterogeneous Problems. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_46
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DOI: https://doi.org/10.1007/3-540-45712-7_46
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