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    GECCO
    2005
    Springer
    207views Optimization» more  GECCO 2005»
    Adaptive crossover and mutation in genetic algorithms based on clustering technique
    14 years 7 days ago
    Adaptive crossover and mutation in genetic algorithms based on clustering technique
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    Instead of having fixed px and pm, this paper presents the use of fuzzy logic to adaptively tune px and pm for optimization of power electronic circuits throughout the process. By...
    Jun Zhang, Henry Shu-Hung Chung, Jinghui Zhong
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    GECCO
    2005
    Springer
    130views Optimization» more  GECCO 2005»
    Quality-time analysis of multi-objective evolutionary algorithms
    14 years 7 days ago
    Quality-time analysis of multi-objective evolutionary algorithms
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    A quality-time analysis of multi-objective evolutionary algorithms (MOEAs) based on schema theorem and building blocks hypothesis is developed. A bicriteria OneMax problem, a hypo...
    Jian-Hung Chen, Shinn-Ying Ho, David E. Goldberg
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    EC
    2000
    96views ECommerce» more  EC 2000»
    Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
    13 years 6 months ago
    Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
    Download  mitpress.mit.edu
    Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied ...
    David A. van Veldhuizen, Gary B. Lamont
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    AI
    1998
    Springer
    107views Artificial Intelligence» more  AI 1998»
    GAS, A Concept on Modeling Species in Genetic Algorithms
    13 years 6 months ago
    GAS, A Concept on Modeling Species in Genetic Algorithms
    Download  www.inf.u-szeged.hu
    This paper introduces a niching technique called GAS (S stands for species) which dynamically creates a subpopulation structure (taxonomic chart) using a radius function instead of...
    Márk Jelasity, József Dombi
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    EMO
    2005
    Springer
    110views Optimization» more  EMO 2005»
    Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms
    14 years 7 days ago
    Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms
    Download  www.ra.cs.uni-tuebingen.de
    Abstract. While Single-Objective Evolutionary Algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective Evo...
    Felix Streichert, Holger Ulmer, Andreas Zell
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