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    EUROGP   2004 European Workshop on Genetic Programming
    Wall of Fame | Most Viewed EUROGP-2004 Paper
    EUROGP
    2004
    Springer
    170views Optimization» more  EUROGP 2004»
    Comparing Hybrid Systems to Design and Optimize Artificial Neural Networks
    13 years 9 months ago
    Comparing Hybrid Systems to Design and Optimize Artificial Neural Networks
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    Abstract. In this paper we conduct a comparative study between hybrid methods to optimize multilayer perceptrons: a model that optimizes the architecture and initial weights of mul...
    Pedro A. Castillo Valdivieso, Maribel Garcí...
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    Comparing Hybrid Systems to Design and Optimize Artificial Neural Networks
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    Exploiting Reflection in Object Oriented Genetic Programming
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    3Download preprint from source145
    Toward an Alternative Comparison between Different Genetic Programming Systems
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    4Download preprint from source135
    Reusing Code in Genetic Programming
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    5Download preprint from source133
    Lymphoma Cancer Classification Using Genetic Programming with SNR Features
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    6Download preprint from source129
    Evolution and Acquisition of Modules in Cartesian Genetic Programming
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    7Download preprint from source123
    Global Distributed Evolution of L-Systems Fractals
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    Boosting Technique for Combining Cellular GP Classifiers
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    9Download preprint from source118
    Grammatical Evolution by Grammatical Evolution: The Evolution of Grammar and Genetic Code
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    10Download preprint from source109
    Extending Grammatical Evolution to Evolve Digital Surfaces with Genr8
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    11Download preprint from source108
    Genetic Programming for Natural Language Parsing
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    12Download preprint from source106
    The Evolution of Concurrent Control Software Using Genetic Programming
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    13Download preprint from source105
    Evolved Matrix Operations for Post-processing Protein Secondary Structure Predictions
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    14Download preprint from source97
    Genetic Programming Applied to Mixed Integer Programming
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    Constrained Molecular Dynamics as a Search and Optimization Tool
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    On the Evolution of Evolutionary Algorithms
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