Titlebook: Evolutionary Multi-Criterion Optimization; 11th International C Hisao Ishibuchi,Qingfu Zhang,Aimin Zhou Conference proceedings 2021 Springe

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书目名称Evolutionary Multi-Criterion Optimization影响因子(影响力)




书目名称Evolutionary Multi-Criterion Optimization影响因子(影响力)学科排名




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书目名称Evolutionary Multi-Criterion Optimization被引频次




书目名称Evolutionary Multi-Criterion Optimization被引频次学科排名




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书目名称Evolutionary Multi-Criterion Optimization年度引用学科排名




书目名称Evolutionary Multi-Criterion Optimization读者反馈




书目名称Evolutionary Multi-Criterion Optimization读者反馈学科排名

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Pitfalls in Experimental Economicss. We propose a decomposition-based multi-objective evolutionary algorithm for solving MMOP (MOEA/D-MM). Experimental results on benchmarks show that MOEA/D-MM is more effective than some well-known traditional multi-objective evolutionary algorithms on MMOP.

INCUR

https://doi.org/10.1007/978-94-009-5767-1imization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives.

Arthr-

MOEA/D for Multiple Multi-objective Optimizations. We propose a decomposition-based multi-objective evolutionary algorithm for solving MMOP (MOEA/D-MM). Experimental results on benchmarks show that MOEA/D-MM is more effective than some well-known traditional multi-objective evolutionary algorithms on MMOP.

Arthr-

Diversity-Driven Selection Operator for Combinatorial Optimizationimization problems, showing that it outperforms five classical selection schemes with regard to solution quality and convergence speed. Besides, the Diversity Driven selection operator delivers good and considerably different solutions in the final population, which can be useful as design alternatives.

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0302-9743 ti-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications..978-3-030-72061-2978-3-030-72062-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

Condyle

EWA Learning in Bilateral Call Marketsl algorithms are not always inferior to the state of the arts, and all the algorithms considered in this paper face some unexpected challenges when dealing with irregularity of Pareto-optimal front. The findings suggest that a systematic evaluation and analysis is needed for any newly-developed algorithms to avoid biases.