Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.
Автор: Shengxiang Yang; Xin Yao Название: Evolutionary Computation for Dynamic Optimization Problems ISBN: 3642384153 ISBN-13(EAN): 9783642384158 Издательство: Springer Рейтинг: Цена: 32652.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.
Автор: Shengxiang Yang; Xin Yao Название: Evolutionary Computation for Dynamic Optimization Problems ISBN: 3642448437 ISBN-13(EAN): 9783642448430 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.
Автор: Emrouznejad Название: Big Data Optimization: Recent Developments and Challenges ISBN: 3319302639 ISBN-13(EAN): 9783319302638 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Themain objective of this book is to provide the necessary background to work withbig data by introducing some novel optimization algorithms and codes capable ofworking in the big data setting as well as introducing some applications in bigdata optimization for both academics and practitioners interested, and tobenefit society, industry, academia, and government. Presenting applications ina variety of industries, this book will be useful for the researchers aiming toanalyses large scale data. Several optimization algorithms for big dataincluding convergent parallel algorithms, limited memory bundle algorithm,diagonal bundle method, convergent parallel algorithms, network analytics, andmany more have been explored in this book.
Описание: Recent Advances in Swarm Intelligence and Evolutionary Computation
Автор: Gilberto Reynoso Meza; Xavier Blasco Ferragud; Jav Название: Controller Tuning with Evolutionary Multiobjective Optimization ISBN: 3319823175 ISBN-13(EAN): 9783319823171 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Поставка под заказ.
Описание: This book is devoted to Multiobjective Optimization Design (MOOD) procedures for controller tuning applications, by means of Evolutionary Multiobjective Optimization (EMO).
Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.
Автор: Daniel Ashlock Название: Evolutionary Computation for Modeling and Optimization ISBN: 1441919694 ISBN-13(EAN): 9781441919694 Издательство: Springer Рейтинг: Цена: 10055.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets.
Lots of applications and test problems, including a biotechnology chapter.
Автор: Liefooghe Название: Evolutionary Computation in Combinatorial Optimization ISBN: 3319774484 ISBN-13(EAN): 9783319774480 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Arnaud Liefooghe; Lu?s Paquete Название: Evolutionary Computation in Combinatorial Optimization ISBN: 3030167100 ISBN-13(EAN): 9783030167103 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* 2019 events EuroGP, EvoMUSART and EvoApplications.The 14 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration. Applications cover domains such as scheduling, routing, partitioning and general graph problems.
Автор: Emrouznejad Ali Название: Big Data Optimization: Recent Developments and Challenges ISBN: 331980765X ISBN-13(EAN): 9783319807652 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
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