Adaptive Differential Evolution: A Robust Approach to Multimodal Problem Optimization (Adaptation, Learning, and Optimization, 1) - Softcover
Book 1 of 12: Adaptation, Learning, and Optimization
Synopsis
The fundamental theme of this book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. The book offers real-world insights into a variety of large-scale complex industrial applications.
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From the Back Cover
Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions.
Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include:
- Theoretical analysis of differential evolution and its control parameters
- Algorithmic design and comparative analysis of parameter adaptive schemes
- Scalability analysis of adaptive differential evolution
- Adaptive differential evolution for multi-objective optimization
- Incorporation of surrogate model for computationally expensive optimization
- Application to winner determination in combinatorial auctions of E-Commerce
- Application to flight route planning in Air Traffic Management
- Application to transition probability matrix optimization in credit-decision making
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Adaptive Differential Evolution: A Robust Approach to Multimodal Problem Optimization (Adaptation, Learning, and Optimization, 1)
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions.Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include:Theoretical analysis of differential evolution and its control parametersAlgorithmic design and comparative analysis of parameter adaptive schemesScalability analysis of adaptive differential evolutionAdaptive differential evolution for multi-objective optimizationIncorporation of surrogate model for computationally expensive optimizationApplication to winner determination in combinatorial auctions of E-CommerceApplication to flight route planning in Air Traffic ManagementApplication to transition probability matrix optimization in credit-decision making 180 pp. Englisch. Seller Inventory # 9783642260216
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Taschenbuch. Condition: Neu. Adaptive Differential Evolution | A Robust Approach to Multimodal Problem Optimization | Arthur C. Sanderson (u. a.) | Taschenbuch | xiii | Englisch | 2012 | Springer-Verlag GmbH | EAN 9783642260216 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 106478690
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Adaptive Differential Evolution
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Taschenbuch. Condition: Neu. Neuware -I rst met Jingqiao when he had just commenced his PhD research in evolutionary algorithms with Arthur Sanderson at Rensselaer. Jingqiaös goals then were the investigation and development of a novel class of se- adaptivedi erentialevolutionalgorithms,later calledJADE. I had remarked to Jingqiao then that Arthur always appreciated strong theoretical foun- tions in his research, so Jingqiaös prior mathematically rigorous work in communications systems would be very useful experience. Later in 2007, whenJingqiaohadcompletedmostofthetheoreticalandinitialexperimental work on JADE, I invited him to spend a year at GE Global Research where he applied his developments to several interesting and important real-world problems. Most evolutionary algorithm conferences usually have their share of in- vative algorithm oriented papers which seek to best the state of the art - gorithms. The best algorithms of a time-frame create a foundation for a new generationof innovativealgorithms, and so on, fostering a meta-evolutionary search for superior evolutionary algorithms. In the past two decades, during whichinterest andresearchin evolutionaryalgorithmshavegrownworldwide by leaps and bounds, engaging the curiosity of researchers and practitioners frommanydiversescienceandtechnologycommunities,developingstand- out algorithms is getting progressively harder.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch. Seller Inventory # 9783642260216