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

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發(fā)表于 2025-3-21 20:00:54 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Artificial Evolution
期刊簡稱10th International C
影響因子2023Jin-Kao Hao,Pierrick Legrand,Marc Schoenauer
視頻videohttp://file.papertrans.cn/163/162025/162025.mp4
發(fā)行地址Up-to-date results.Fast-track conference proceedings.State-of-the-art research
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Evolution; 10th International C Jin-Kao Hao,Pierrick Legrand,Marc Schoenauer Conference proceedings 2012 Springer-Verlag Berlin
影響因子This book constitutes selected best papers from the 10th International Conference on Artificial Evolution, EA 2011, held in Angers, France, in October 2011. Initially, 33 full papers and 10 post papers were carefully reviewed and selected from 64 submissions. This book presents the 19 best papers selected from these contributions. The papers are organized in topical sections on ant colony optimization; multi-objective optimization; analysis; implementation and robotics; combinatorial optimization; learning and parameter tuning; new nature inspired models; probabilistic algorithms; theory and evolutionary search; and applications.
Pindex Conference proceedings 2012
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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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