A Brief Introduction to Continuous Evolutionary Optimization, Oliver Kramer

Автор: Efr?n Mezura-Montes
Название: Constraint-Handling in Evolutionary Optimization
ISBN: 3642101550 ISBN-13(EAN): 9783642101557
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.

Автор: Xinjie Yu; Mitsuo Gen
Название: Introduction to Evolutionary Algorithms
ISBN: 144712569X ISBN-13(EAN): 9781447125693
Издательство: Springer
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Цена: 181670.00 T
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Описание: Introduction to Evolutionary Algorithms presents a comprehensive, up-to-date overview of evolutionary algorithms. Readers will find a discussion of hot topics in the field, including genetic algorithms, differential evolution, swarm intelligence, and artificial immune systems.

Автор: Marks Ii Robert J Et Al
Название: Introduction To Evolutionary Informatics
ISBN: 9813142146 ISBN-13(EAN): 9789813142145
Издательство: World Scientific Publishing
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Цена: 50690.00 T
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Описание:

Science has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature.

Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information.

This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory.

Автор: Ertel
Название: Introduction to Artificial Intelligence
ISBN: 0857292986 ISBN-13(EAN): 9780857292988
Издательство: Springer
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Цена: 32560.00 T
Наличие на складе: Невозможна поставка.
Описание: This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.

Автор: H.B. Mitchell
Название: Multi-Sensor Data Fusion: An Introduction
ISBN: 3642090672 ISBN-13(EAN): 9783642090677
Издательство: Springer
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Цена: 87060.00 T
Наличие на складе: Поставка под заказ.
Описание: This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended.

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: 97820.00 T
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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: 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: 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: M.C. Bhuvaneswari
Название: Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems
ISBN: 8132219570 ISBN-13(EAN): 9788132219576
Издательство: Springer
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Цена: 130430.00 T
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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: Hime Aguiar e Oliveira Junior
Название: Evolutionary Global Optimization, Manifolds and Applications
ISBN: 3319264664 ISBN-13(EAN): 9783319264660
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book presents powerful techniques for solving global optimization problems on manifolds by means of evolutionary algorithms, and shows in practice how these techniques can be applied to solve real-world problems. Here, these problems are reformulated as constrained global optimization tasks and solved with the help of Fuzzy ASA.

Автор: Xinjie Yu; Mitsuo Gen
Название: Introduction to Evolutionary Algorithms
ISBN: 184996128X ISBN-13(EAN): 9781849961288
Издательство: Springer
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Цена: 181670.00 T
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Описание: Introduction to Evolutionary Algorithms presents a comprehensive, up-to-date overview of evolutionary algorithms. Readers will find a discussion of hot topics in the field, including genetic algorithms, differential evolution, swarm intelligence, and artificial immune systems.

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.