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    » On the Brittleness of Evolutionary Algorithms
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    ACIVS
    2009
    Springer
    229views Computer Vision» more  ACIVS 2009»
    Engineering of Computer Vision Algorithms Using Evolutionary Algorithms
    14 years 1 months ago
    Engineering of Computer Vision Algorithms Using Evolutionary Algorithms
    Download  www.ra.cs.uni-tuebingen.de
    Abstract. Computer vision algorithms are currently developed by looking up the available operators from the literature and then arranging those operators such that the desired task...
    Marc Ebner
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    TKDE
    2012
    270views Formal Methods» more  TKDE 2012»
    Low-Rank Kernel Matrix Factorization for Large-Scale Evolutionary Clustering
    11 years 11 months ago
    Low-Rank Kernel Matrix Factorization for Large-Scale Evolutionary Clustering
    Download  www.cs.wayne.edu
    —Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorith...
    Lijun Wang, Manjeet Rege, Ming Dong, Yongsheng Din...
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    GECCO
    2006
    Springer
    186views Optimization» more  GECCO 2006»
    Comparison of multi-modal optimization algorithms based on evolutionary algorithms
    14 years 7 days ago
    Comparison of multi-modal optimization algorithms based on evolutionary algorithms
    Download  www.cs.bham.ac.uk
    Many engineering optimization tasks involve finding more than one optimum solution. The present study provides a comprehensive review of the existing work done in the field of mul...
    Gulshan Singh, Kalyanmoy Deb
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    IADIS
    2008
    141views Internet Technology» more  IADIS 2008»
    Gapbnf_Rule: A Genetic Miner Rule
    13 years 10 months ago
    Gapbnf_Rule: A Genetic Miner Rule
    Download  www.iadis.net
    The aim of this paper is to present an evolutionary system for classification problem called GAPBNF_Rule. GAPBNF_Rule is a powerful evolutionary method that distinguishes between ...
    Rasha Shakir Abdul-Wahhab
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    GECCO
    2006
    Springer
    130views Optimization» more  GECCO 2006»
    An efficient multi-objective evolutionary algorithm with steady-state replacement model
    14 years 7 days ago
    An efficient multi-objective evolutionary algorithm with steady-state replacement model
    Download  www.cs.bham.ac.uk
    The generic Multi-objective Evolutionary Algorithm (MOEA) aims to produce Pareto-front approximations with good convergence and diversity property. To achieve convergence, most mu...
    Dipti Srinivasan, Lily Rachmawati
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