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

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書目名稱Artificial Evolution影響因子(影響力)




書目名稱Artificial Evolution影響因子(影響力)學(xué)科排名




書目名稱Artificial Evolution網(wǎng)絡(luò)公開度




書目名稱Artificial Evolution網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Evolution被引頻次




書目名稱Artificial Evolution被引頻次學(xué)科排名




書目名稱Artificial Evolution年度引用




書目名稱Artificial Evolution年度引用學(xué)科排名




書目名稱Artificial Evolution讀者反饋




書目名稱Artificial Evolution讀者反饋學(xué)科排名

Glucocorticoids

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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decipher

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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GROUP

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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