Evolutionary Multi-Criterion Optimization, Ant?nio Gaspar-Cunha; Carlos Henggeler Antunes; Ca

Автор: Ant?nio Gaspar-Cunha; Carlos Henggeler Antunes; Ca
Название: Evolutionary Multi-Criterion Optimization
ISBN: 331915933X ISBN-13(EAN): 9783319159331
Издательство: Springer
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Цена: 8944.00 р.
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Описание: This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 held in Guimaraes, Portugal in March/April 2015.

Автор: Eckart Zitzler; Kalyanmoy Deb; Lothar Thiele; Carl
Название: Evolutionary Multi-Criterion Optimization
ISBN: 3540417451 ISBN-13(EAN): 9783540417453
Издательство: Springer
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Цена: 18167.00 р.
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Описание: A collection of papers organized in sections on algorithm improvements, performance assessment and comparison, constraint handling and problem decomposition, uncertainty and noise, hybrid and alternative methods, scheduling, and applications of multi-objective optimization in a variety of fields.

Автор: Joanna Ko?odziej
Название: Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems
ISBN: 3642436617 ISBN-13(EAN): 9783642436611
Издательство: Springer
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Цена: 15672.00 р.
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Описание: This book explores hot topics in the design, administration and management of dynamic grid environments. The emphasis is on preferences and autonomous decisions of system users, secure access to processed data and services and the use of green technologies.

Автор: Cengiz Kahraman
Название: Fuzzy Multi-Criteria Decision Making
ISBN: 0387768122 ISBN-13(EAN): 9780387768120
Издательство: Springer
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Цена: 25155.00 р.
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Описание: Presents fuzzy multiattribute and multiobjective decision-making methodologies. This work examines various fuzzy multicriteria methods, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjective optimization, and more.

Автор: Zhang
Название: Hesitant Fuzzy Methods for Multiple Criteria Decision Analysis
ISBN: 3319420003 ISBN-13(EAN): 9783319420004
Издательство: Springer
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Цена: 16769.00 р.
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Описание: The book offers a comprehensive introduction to methods for solving multiple criteria decision making and group decision making problems with hesitant fuzzy information. It reports on the authors’ latest research, as well as on others’ research, providing readers with a complete set of decision making tools, such as hesitant fuzzy TOPSIS, hesitant fuzzy TODIM, hesitant fuzzy LINMAP, hesitant fuzzy QUALIFEX, and the deviation modeling approach with heterogeneous fuzzy information. The main focus is on decision making problems in which the criteria values and/or the weights of criteria are not expressed in crisp numbers but are more suitable to be denoted as hesitant fuzzy elements. The largest part of the book is devoted to new methods recently developed by the authors to solve decision making problems in situations where the available information is vague or hesitant. These methods are presented in detail, together with their application to different type of decision-making problems. All in all, the book represents a valuable reference guide for graduate students and researchers in the both fields of fuzzy logic and decision making.

Автор: Yong Shi; Shouyang Wang; Yi Peng; Jianping Li; Yon
Название: Cutting-Edge Research Topics on Multiple Criteria Decision Making
ISBN: 3642022979 ISBN-13(EAN): 9783642022975
Издательство: Springer
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Цена: 22359.00 р.
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Описание: 20th International Conference MCDM 2009 ChengduJiuzhaigou China June 2126 2009 Proceedings. .

Автор: Valery N. Shlyannikov
Название: Elastic-Plastic Mixed-Mode Fracture Criteria and Parameters
ISBN: 364253659X ISBN-13(EAN): 9783642536595
Издательство: Springer
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Цена: 15372.00 р.
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Автор: M.C. Bhuvaneswari
Название: Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems
ISBN: 8132235398 ISBN-13(EAN): 9788132235392
Издательство: Springer
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Цена: 14365.00 р.
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Описание: This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design.

Автор: Carlos M. Fonseca; Xavier Gandibleux; Jin-Kao Hao;
Название: Evolutionary Multi-Criterion Optimization
ISBN: 3642010199 ISBN-13(EAN): 9783642010194
Издательство: Springer
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Цена: 14673.00 р.
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Описание: Constitutes the refereed proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009, held in Nantes, France in April 2009. This book presents 39 revised full papers together with 5 invited talks that were reviewed and selected from 72 submissions.

Автор: Heike Trautmann; G?nter Rudolph; Kathrin Klamroth;
Название: Evolutionary Multi-Criterion Optimization
ISBN: 3319541560 ISBN-13(EAN): 9783319541563
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Munster, Germany in March 2017. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; parallel EMO models; EMO algorithm implementations.

Автор: Constantin Zopounidis; Michael Doumpos
Название: Intelligent Decision Aiding Systems Based on Multiple Criteria for Financial Engineering
ISBN: 079236273X ISBN-13(EAN): 9780792362739
Издательство: Springer
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Цена: 19564.00 р.
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Описание: Provides a point of view on the field of financial engineering, through the application of multicriteria intelligent decision aiding systems. This book explores the adequacy of the tools and systems developed according to this approach in addressing complex financial decision problems, encountered within the field of financial engineering.

Автор: Chi-Keong Goh; Kay Chen Tan
Название: Evolutionary Multi-objective Optimization in Uncertain Environments
ISBN: 3642101135 ISBN-13(EAN): 9783642101137
Издательство: Springer
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Цена: 20962.00 р.
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Описание: The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. The book is intended for a wide readership.