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Titlebook: Advances in Differential Evolution; Uday K. Chakraborty Book 2008 Springer-Verlag Berlin Heidelberg 2008 Analysis.algorithm.algorithms.com

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發(fā)表于 2025-3-21 17:53:40 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Advances in Differential Evolution
影響因子2023Uday K. Chakraborty
視頻videohttp://file.papertrans.cn/148/147806/147806.mp4
發(fā)行地址This book seeks to present a comprehensive study of the state of the art in differential evolution
學科分類Studies in Computational Intelligence
圖書封面Titlebook: Advances in Differential Evolution;  Uday K. Chakraborty Book 2008 Springer-Verlag Berlin Heidelberg 2008 Analysis.algorithm.algorithms.com
影響因子.Differential evolution is arguably one of the hottest topics in today‘s computational intelligence research. This book seeks to present a comprehensive study of the state of the art in this technology and also directions for future research. ..The fourteen chapters of this book have been written by leading experts in the area. The first seven chapters focus on algorithm design, while the last seven describe real-world applications. Chapter 1 introduces the basic differential evolution (DE) algorithm and presents a broad overview of the field. Chapter 2 presents a new, rotationally invariant DE algorithm. The role of self-adaptive control parameters in DE is investigated in Chapter 3. Chapters 4 and 5 address constrained optimization; the former develops suitable stopping conditions for the DE run, and the latter presents an improved DE algorithm for problems with very small feasible regions. A novel DE algorithm, based on the concept of "opposite" points, is the topic of Chapter 6. Chapter 7 provides a survey of multi-objective differential evolution algorithms. A review of the major application areas of differential evolution is presented in Chapter 8. Chapter 9 discusses the app
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Phillip Cem Cezayirli,Hatice Türe,U?ur Türe of the opposite numbers is empirically verified. Additionally, two time varying models for control parameter adjustment of ODE are investigated. Details of the ODE algorithm, the test set, and the comparison strategy are provided.
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Stephan A. Munich M.D.,Michael Chen M.D. an alternative to perform online optimization, where the input variables are adjusted while the real fermentation process is ongoing. In this case, a changing landscape is optimized, therefore making the task of the algorithms more difficult. However, that fact does not impair the performance of the DE and confirms its good behaviour.
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