Meta-heuristic Optimization Techniques, 

Автор: Stefan Edelkamp
Название: Heuristic Search,
ISBN: 0123725127 ISBN-13(EAN): 9780123725127
Издательство: Elsevier Science
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Цена: 66690.00 T
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Описание: Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. This title presents a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems.

Автор: Emiliano Ippoliti
Название: Heuristic Reasoning
ISBN: 3319362224 ISBN-13(EAN): 9783319362229
Издательство: Springer
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Цена: 95770.00 T
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Описание: And how can we use findings about scientific discovery to boost funding policies, thus fostering a deeper impact of scientific discovery itself?The respective chapters in this book provide readers with answers to these questions.

Автор: Castillo Oscar, Rodriguez Luis
Название: A New Meta-Heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics
ISBN: 3030822877 ISBN-13(EAN): 9783030822873
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book focuses on the fields of nature-inspired algorithms, optimization problems and fuzzy logic. In this book, a new metaheuristic based on String Theory from Physics is proposed. It is important to mention that we have proposed the new algorithm to generate new potential solutions in optimization problems in order to find new ways that could improve the results in solving these problems. We are presenting the results for the proposed method in different cases of study. The first case, is optimization of traditional benchmark mathematical functions. The second case, is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting results of the CEC 2017 Competition on Constrained Real-Parameter Optimization that are problems that contain the presence of constraints that alter the shape of the search space making them more difficult to solve. Finally, in the third case, we are presenting the optimization of a fuzzy inference system, specifically for finding the optimal design of a fuzzy controller for an autonomous mobile robot. It is important to mention that in all study cases we are presenting statistical tests in or-der to validate the performance of proposed method. In summary, we believe that this book will be of great interest to a wide audience, ranging from engineering and science graduate students, to researchers and professors in computational intelligence, metaheuristics, optimization, robotics and control.

Автор: Eftimov
Название: Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
ISBN: 3030969169 ISBN-13(EAN): 9783030969165
Издательство: Springer
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Цена: 130430.00 T
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Описание: Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis – Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms – Chapters 5-7. Part III: Implementation and application of Deep Statistical Comparison – Chapter 8.

Автор: Salhi Saпd
Название: Heuristic Search: The Emerging Science of Problem Solving
ISBN: 3319841432 ISBN-13(EAN): 9783319841434
Издательство: Springer
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Цена: 111790.00 T
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Описание: This book aims to provide a general overview of heuristic search, to present the basic steps of the most popular heuristics, and to stress their hidden difficulties as well as their opportunities.

Автор: Dmitri A. Viattchenin
Название: A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
ISBN: 364244301X ISBN-13(EAN): 9783642443015
Издательство: Springer
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Цена: 113180.00 T
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Описание: In a new approach to possibilistic clustering, the sought clustering structure of the set is based directly on the formal definition of fuzzy cluster and possibilistic memberships are determined directly from the values of the pairwise similarity of objects.

Автор: Kumar, Kaushik (associate Professor, Department Of
Название: Optimizing engineering problems through heuristic techniques
ISBN: 1138485365 ISBN-13(EAN): 9781138485365
Издательство: Taylor&Francis
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Цена: 168430.00 T
Наличие на складе: Нет в наличии.
Описание: This book will cover the issues related to optimization of engineering problems using heuristic techniques, with an industrial outlook. It will cover a broad area related to optimization of real life complex engineering problems. It will explore wide perspectives and future directions in industrial engineering research on a global platform.

Автор: Frumen Olivas; Fevrier Valdez; Oscar Castillo; Pat
Название: Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic
ISBN: 3319708503 ISBN-13(EAN): 9783319708508
Издательство: Springer
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Цена: 51230.00 T
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Описание: Introduction.- Theory and Background.- Problems Statement.- Methodology.- Simulation Results.- Statistical Analysis and Comparison of Results.

Автор: Dietmar G. Maringer
Название: Portfolio Management with Heuristic Optimization
ISBN: 1441938427 ISBN-13(EAN): 9781441938428
Издательство: Springer
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Цена: 158380.00 T
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Описание: The second part (Applications and Contributions) consists of five chapters, covering different problems in financial optimization: the effects of (linear, proportional and combined) transaction costs together with integer constraints and limitations on the initital endowment to be invested;

Автор: Wolfgang Bibel; Pallab Dasgupta; Rudolf Kruse; P.
Название: Multiobjective Heuristic Search
ISBN: 3528057084 ISBN-13(EAN): 9783528057084
Издательство: Springer
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Цена: 81050.00 T
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Описание: Solutions to most real-world optimization problems involve a trade-offbetween multiple conflicting and non-commensurate objectives. Some ofthe most challenging ones are area-delay trade-off in VLSI synthesisand design space exploration, time-space trade-off in computation, andmulti-strategy games.

Автор: Viattchenin Dmitri A
Название: Heuristic Approach to Possibilistic Clustering: Algorithms a
ISBN: 3642355358 ISBN-13(EAN): 9783642355356
Издательство: Springer
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Цена: 130610.00 T
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Описание: In a new approach to possibilistic clustering, the sought clustering structure of the set is based directly on the formal definition of fuzzy cluster and possibilistic memberships are determined directly from the values of the pairwise similarity of objects.

Автор: Amitava Chatterjee; Hadi Nobahari; Patrick Siarry
Название: Advances in Heuristic Signal Processing and Applications
ISBN: 364244525X ISBN-13(EAN): 9783642445255
Издательство: Springer
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Цена: 121110.00 T
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Описание:

There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems.

In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm intelligence based techniques. The applications considered are in domains such as communications engineering, estimation and tracking, digital filter design, wireless sensor networks, bioelectric signal classification, image denoising, and image feature tracking.

The book presents interesting, state-of-the-art methodologies for solving real-world problems and it is a suitable reference for researchers and engineers in the areas of heuristics and signal processing.