Theory of Evolutionary Computation: Recent Developments in Discrete Optimization, Doerr Benjamin, Neumann Frank

Автор: Manuel Barros; Jorge Guilherme; Nuno Horta
Название: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques
ISBN: 3642263232 ISBN-13(EAN): 9783642263231
Издательство: Springer
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Цена: 158380.00 T
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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: Bin Hu; Manuel L?pez-Ib??ez
Название: Evolutionary Computation in Combinatorial Optimization
ISBN: 3319554522 ISBN-13(EAN): 9783319554525
Издательство: Springer
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Цена: 51230.00 T
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Описание: A Computational Study of Neighborhood Operators for Job-shop Scheduling Problems with Regular Objectives.- A Genetic Algorithm for Multi-Component Optimization Problems: the Case of the Travelling Thief Problem.- A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search.- A Memetic Algorithm to Maximise the Employee Substitutability in Personnel Shift Scheduling.- Construct, Merge, Solve and Adapt versus Large Neighborhood Search for Solving the Multi-Dimensional Knapsack Problem: Which One Works Better When.- Decomposing SAT Instances with Pseudo Backbones.- Efficient Consideration of Soft Time Windows in a Large Neighborhood Search for the Districting and Routing Problem for Security Control.- Estimation of Distribution Algorithms for the Firefighter Problem.- LCS-Based Selective Route Exchange Crossover for the Pickup and Delivery Problem with Time Windows.- Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO.- Optimizing Charging Station Locations for Electric Car-Sharing Systems.- Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier.- Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants.- The Weighted Independent Domination Problem: ILP Model and Algorithmic

Автор: Oliver Kramer
Название: A Brief Introduction to Continuous Evolutionary Optimization
ISBN: 3319034219 ISBN-13(EAN): 9783319034218
Издательство: Springer
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Цена: 60940.00 T
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Описание: This book introduces heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. It presents heuristic extensions that allow optimization in constrained, multimodal and multi-objective solution spaces.

Автор: Manuel Barros; Jorge Guilherme; Nuno Horta
Название: Analog Circuits and Systems Optimization based on Evolutionary Computation Techniques
ISBN: 3642123457 ISBN-13(EAN): 9783642123450
Издательство: Springer
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Цена: 158380.00 T
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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
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Цена: 217680.00 T
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Описание: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.

Автор: Daniel Ashlock
Название: Evolutionary Computation for Modeling and Optimization
ISBN: 1441919694 ISBN-13(EAN): 9781441919694
Издательство: Springer
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Цена: 67030.00 T
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Описание:

Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets.

Автор: Liefooghe
Название: Evolutionary Computation in Combinatorial Optimization
ISBN: 3319774484 ISBN-13(EAN): 9783319774480
Издательство: Springer
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Цена: 46570.00 T
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Автор: Shengxiang Yang; Xin Yao
Название: Evolutionary Computation for Dynamic Optimization Problems
ISBN: 3642448437 ISBN-13(EAN): 9783642448430
Издательство: Springer
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Цена: 174130.00 T
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Описание: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.

Автор: Arnaud Liefooghe; Lu?s Paquete
Название: Evolutionary Computation in Combinatorial Optimization
ISBN: 3030167100 ISBN-13(EAN): 9783030167103
Издательство: Springer
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Цена: 46570.00 T
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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
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Цена: 158380.00 T
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Автор: Bechikh
Название: Recent Advances in Evolutionary Multi-objective Optimization
ISBN: 3319429779 ISBN-13(EAN): 9783319429779
Издательство: Springer
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Цена: 111790.00 T
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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.

Автор: Emrouznejad
Название: Big Data Optimization: Recent Developments and Challenges
ISBN: 3319302639 ISBN-13(EAN): 9783319302638
Издательство: Springer
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Цена: 139310.00 T
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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.