Metaheuristic Procedures for Training Neural Networks PDF Download

Author: Enrique Alba
Publisher: Springer Science & Business Media
ISBN: 0387334165
Category : Business & Economics
Languages : en
Pages : 258

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Book Description
This book provides successful implementations of metaheuristic methods for neural network training. It is the first book to achieve this objective. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Overall, the book's aim is to provide a broad coverage of the concepts, methods, and tools of the important area of ANNs within the realm of continuous optimization.

Author: Diego Oliva
Publisher:
ISBN: 9783030705435
Category :
Languages : en
Pages : 0

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Book Description
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Author: Diego Oliva
Publisher: Springer Nature
ISBN: 3030705420
Category : Computational intelligence
Languages : en
Pages : 765

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Book Description
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Author: Zhonghuan Tian
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages :

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Book Description
Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchical layers. DL is implemented by deep neural network (DNN) which has multi-hidden layers. DNN is developed from traditional artificial neural network (ANN). However, in the training process of DL, it has certain inefficiency due to very long training time required. Meta-heuristic aims to find good or near-optimal solutions at a reasonable computational cost. In this article, meta-heuristic algorithms are reviewed, such as genetic algorithm (GA) and particle swarm optimization (PSO), for traditional neural network's training and parameter optimization. Thereafter the possibilities of applying meta-heuristic algorithms on DL training and parameter optimization are discussed.

Author: Dey, Nilanjan
Publisher: IGI Global
ISBN: 1522541527
Category : Computers
Languages : en
Pages : 357

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Book Description
Metaheuristic algorithms are present in various applications for different domains. Recently, researchers have conducted studies on the effectiveness of these algorithms in providing optimal solutions to complicated problems. Advancements in Applied Metaheuristic Computing is a crucial reference source for the latest empirical research on methods and approaches that include metaheuristics for further system improvements, and it offers outcomes of employing optimization algorithms. Featuring coverage on a broad range of topics such as manufacturing, genetic programming, and medical imaging, this publication is ideal for researchers, academicians, advanced-level students, and technology developers seeking current research on the use of optimization algorithms in several applications.

Author: Fouad Bennis
Publisher: Springer Nature
ISBN: 3030264580
Category : Business & Economics
Languages : en
Pages : 503

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Book Description
This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.

Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668424096
Category : Computers
Languages : en
Pages : 1575

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Book Description
Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.

Author: Ozgur Baskan
Publisher: BoD – Books on Demand
ISBN: 9535125923
Category : Mathematics
Languages : en
Pages : 326

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Book Description
This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field. It consists of 13 chapters divided into two parts: (I) Engineering applications, which presents some new applications of different methods, and (II) Applications in various areas, where recent contributions of state-of-the-art optimization methods to diverse fields are presented.

Author: Yoshua Bengio
Publisher: Now Publishers Inc
ISBN: 1601982941
Category : Computational learning theory
Languages : en
Pages : 145

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Book Description
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Author: El-Ghazali Talbi
Publisher: John Wiley & Sons
ISBN: 0470496908
Category : Computers
Languages : en
Pages : 625

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Book Description
A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.