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7 Result(s)

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  1. No Access

    Chapter and Conference Paper

    Geometric Differential Evolution in MOEA/D: A Preliminary Study

    The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based algorithm which has became successful for solving multi-objective optimization problems (MOPs). So far, for th...

    Saúl Zapotecas-Martínez, Bilel Derbel… in Advances in Artificial Intelligence and So… (2015)

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    Chapter and Conference Paper

    Adaptive Control of the Number of Crossed Genes in Many-Objective Evolutionary Optimization

    To realize effective genetic operation in evolutionary many-objective optimization, crossover controlling the number of crossed genes (CCG) has been proposed. CCG controls the number of crossed genes by using ...

    Hiroyuki Sato, Carlos A. Coello Coello… in Learning and Intelligent Optimization (2012)

  3. No Access

    Chapter and Conference Paper

    A Study on Large Population MOEA Using Adaptive ε-Box Dominance and Neighborhood Recombination for Many-Objective Optimization

    Multi-objective evolutionary algorithms are increasingly being investigated to solve many-objective optimization problems. However, most algorithms recently proposed for many-objective optimization cannot find...

    Naoya Kowatari, Akira Oyama, Hernán E. Aguirre… in Learning and Intelligent Optimization (2012)

  4. No Access

    Chapter and Conference Paper

    Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization

    In this work, we analyze genetic diversity of Pareto optimal solutions (POS) and study effective crossover operators in evolutionary many-objective optimization. First we examine the diversity of genes in the ...

    Hiroyuki Sato, Hernán E. Aguirre, Kiyoshi Tanaka in Learning and Intelligent Optimization (2011)

  5. No Access

    Chapter and Conference Paper

    Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs

    This work proposes a method to control the dominance area of solutions in order to induce appropriate ranking of solutions for the problem at hand, enhance selection, and improve the performance of MOEAs on co...

    Hiroyuki Sato, Hernán E. Aguirre… in Evolutionary Multi-Criterion Optimization (2007)

  6. No Access

    Chapter and Conference Paper

    Selection, Drift, Recombination, and Mutation in Multiobjective Evolutionary Algorithms on Scalable MNK-Landscapes

    This work focuses on the working principles, behavior, and performance of state of the art multiobjective evolutionary algorithms (MOEAs) on discrete search spaces by using MNK-Landscapes. Its motivation comes...

    Hernán E. Aguirre, Kiyoshi Tanaka in Evolutionary Multi-Criterion Optimization (2005)

  7. No Access

    Chapter and Conference Paper

    Genetic Algorithms on NK-Landscapes: Effects of Selection, Drift, Mutation, and Recombination

    Empirical studies have shown that the overall performance of random bit climbers on NK-Landscapes is superior to the performance of some simple and enhanced GAs. Analytical studies have also lead to suggest th...

    Hernán E. Aguirre, Kiyoshi Tanaka in Applications of Evolutionary Computing (2003)