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Titlebook: Applications of Evolutionary Computation; EvoApplications 2011 Cecilia Chio,Stefano Cagnoni,Georgios N. Yannakaki Conference proceedings 20

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發(fā)表于 2025-3-21 18:02:11 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Applications of Evolutionary Computation
期刊簡稱EvoApplications 2011
影響因子2023Cecilia Chio,Stefano Cagnoni,Georgios N. Yannakaki
視頻videohttp://file.papertrans.cn/160/159394/159394.mp4
發(fā)行地址Unique visibility.State-of-the-art research.Fast-track conference proceedings
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Applications of Evolutionary Computation; EvoApplications 2011 Cecilia Chio,Stefano Cagnoni,Georgios N. Yannakaki Conference proceedings 20
影響因子This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo* 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and 6625). The present volume contains contributions for EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC. The 36 revised full papers presented were carefully reviewed and selected from numerous submissions. This volume presents an overview about the latest research in EC. Areas where evolutionary computation techniques have been applied range from telecommunication networks to complex systems, finance and economics, games, image analysis, evolutionary music and art, parameter optimization, scheduling, and logistics. These papers may provide guidelines to help new researchers tackling their own problem using EC.
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0302-9743 onal Conference on the Applications of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo* 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and
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Evolving Interesting Maps for a First Person Shooterlayers’ average fighting time. For that purpose, four different map representations are tested and compared. Results obtained showcase the clear advantage of some representations in generating interesting FPS maps and demonstrate the promise of the approach followed for automatic level design in that game genre.
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Training Neural Networks to Play Backgammon Variants Using Reinforcement Learningg and Neural Network function approximation we train agents that learn a game position evaluation function for these games. We show that the resulting agents significantly outperform the open-source program Tavli3D.
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