Titlebook: Artificial Evolution; 10th International C Jin-Kao Hao,Pierrick Legrand,Marc Schoenauer Conference proceedings 2012 Springer-Verlag Berlin

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书目名称Artificial Evolution影响因子(影响力)




书目名称Artificial Evolution影响因子(影响力)学科排名




书目名称Artificial Evolution网络公开度




书目名称Artificial Evolution网络公开度学科排名




书目名称Artificial Evolution被引频次




书目名称Artificial Evolution被引频次学科排名




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书目名称Artificial Evolution年度引用学科排名




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书目名称Artificial Evolution读者反馈学科排名

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A Surrogate-Based Intelligent Variation Operator for Multiobjective Optimizationoblems (MOPs). However, in order to achieve acceptable results, Multiobjective Evolutionary Algorithms (MOEAs) usually require several evaluations of the optimization function. Moreover, when each of these evaluations represents a high computational cost, these expensive problems remain intractable

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A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsizeosed for multimodal optimization, and many of them are based on restart strategies. However, only few works address the issue of initialization in restarts. Furthermore, very few comparisons have been done, between different MMO algorithms, and against simple baseline methods. This paper proposes an

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Improving Performance via Population Growth and Local Search: The Case of the Artificial Bee Colony olutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization benchmark problems. This is not the first time we see that the two aforementioned modifications make an initially non-competitive algorithm obtain state-of-the-art results. In previous work, we

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