Evolutionary Algorithms And Neural Networks

Evolutionary Algorithms And Neural Networks Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Evolutionary Algorithms And Neural Networks book. This book definitely worth reading, it is an incredibly well-written.

Evolutionary Algorithms and Neural Networks

Seyedali Mirjalili
Evolutionary Algorithms and Neural Networks Book PDF

Author : Seyedali Mirjalili
Publisher : Springer
Page : 156 pages
File Size : 50,7 Mb
Release : 2018-06-26
Category : Technology & Engineering
ISBN : 9783319930251

Get Book

Evolutionary Algorithms and Neural Networks by Seyedali Mirjalili Pdf

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.

Evolutionary Learning Algorithms for Neural Adaptive Control

Dimitris C. Dracopoulos
Evolutionary Learning Algorithms for Neural Adaptive Control Book PDF

Author : Dimitris C. Dracopoulos
Publisher : Springer
Page : 214 pages
File Size : 50,9 Mb
Release : 2013-12-21
Category : Computers
ISBN : 9781447109037

Get Book

Evolutionary Learning Algorithms for Neural Adaptive Control by Dimitris C. Dracopoulos Pdf

Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.

Deep Neural Evolution

Hitoshi Iba,Nasimul Noman
Deep Neural Evolution Book PDF

Author : Hitoshi Iba,Nasimul Noman
Publisher : Springer Nature
Page : 437 pages
File Size : 53,5 Mb
Release : 2020-05-20
Category : Computers
ISBN : 9789811536854

Get Book

Deep Neural Evolution by Hitoshi Iba,Nasimul Noman Pdf

This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.

NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS

S. RAJASEKARAN,G.A. VIJAYALAKSHMI PAI
NEURAL NETWORKS FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS SYNTHESIS AND APPLICATIONS Book PDF

Author : S. RAJASEKARAN,G.A. VIJAYALAKSHMI PAI
Publisher : PHI Learning Pvt. Ltd.
Page : 576 pages
File Size : 50,9 Mb
Release : 2017-05-01
Category : Computers
ISBN : 9788120353343

Get Book

NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS by S. RAJASEKARAN,G.A. VIJAYALAKSHMI PAI Pdf

The second edition of this book provides a comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence, which in recent years, has turned synonymous to it. The constituent technologies discussed comprise neural network (NN), fuzzy system (FS), evolutionary algorithm (EA), and a number of hybrid systems, which include classes such as neuro-fuzzy, evolutionary-fuzzy, and neuro-evolutionary systems. The hybridization of the technologies is demonstrated on architectures such as fuzzy backpropagation network (NN-FS hybrid), genetic algorithm-based backpropagation network (NN-EA hybrid), simplified fuzzy ARTMAP (NN-FS hybrid), fuzzy associative memory (NN-FS hybrid), fuzzy logic controlled genetic algorithm (EA-FS hybrid) and evolutionary extreme learning machine (NN-EA hybrid) Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering. This book, with a wealth of information that is clearly presented and illustrated by many examples and applications, is designed for use as a text for the courses in soft computing at both the senior undergraduate and first-year postgraduate levels of computer science and engineering. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work.

Evolutionary Approach to Machine Learning and Deep Neural Networks

Hitoshi Iba
Evolutionary Approach to Machine Learning and Deep Neural Networks Book PDF

Author : Hitoshi Iba
Publisher : Springer
Page : 245 pages
File Size : 49,8 Mb
Release : 2018-06-15
Category : Computers
ISBN : 9789811302008

Get Book

Evolutionary Approach to Machine Learning and Deep Neural Networks by Hitoshi Iba Pdf

This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields. Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution. The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.

Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms

Lakhmi C. Jain,N.M. Martin
Fusion of Neural Networks Fuzzy Systems and Genetic Algorithms Book PDF

Author : Lakhmi C. Jain,N.M. Martin
Publisher : CRC Press
Page : 363 pages
File Size : 42,8 Mb
Release : 2020-01-29
Category : Computers
ISBN : 9781000715125

Get Book

Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms by Lakhmi C. Jain,N.M. Martin Pdf

Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another. This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include: direct frequency converters electro-hydraulic systems motor control toaster control speech recognition vehicle routing fault diagnosis Asynchronous Transfer Mode (ATM) communications networks telephones for hard-of-hearing people control of gas turbine aero-engines telecommunications systems design Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

Automatic Generation of Neural Network Architecture Using Evolutionary Computation

E. Vonk,L. C. Jain,Ray P. Johnson
Automatic Generation of Neural Network Architecture Using Evolutionary Computation Book PDF

Author : E. Vonk,L. C. Jain,Ray P. Johnson
Publisher : World Scientific
Page : 196 pages
File Size : 42,6 Mb
Release : 1997
Category : Computers
ISBN : 9810231067

Get Book

Automatic Generation of Neural Network Architecture Using Evolutionary Computation by E. Vonk,L. C. Jain,Ray P. Johnson Pdf

This book describes the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the neural network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. The process of trial and error is not only time-consuming but may not generate an optimal network. The use of evolutionary computation is a step towards automation in neural network architecture generation.An overview of the field of evolutionary computation is presented, together with the biological background from which the field was inspired. The most commonly used approaches to a mathematical foundation of the field of genetic algorithms are given, as well as an overview of the hybridization between evolutionary computation and neural networks. Experiments on the implementation of automatic neural network generation using genetic programming and one using genetic algorithms are described, and the efficacy of genetic algorithms as a learning algorithm for a feedforward neural network is also investigated.

Artificial Neural Nets and Genetic Algorithms

David W. Pearson,Nigel C. Steele,Rudolf Albrecht
Artificial Neural Nets and Genetic Algorithms Book PDF

Author : David W. Pearson,Nigel C. Steele,Rudolf Albrecht
Publisher : Springer Science & Business Media
Page : 274 pages
File Size : 42,7 Mb
Release : 2011-06-28
Category : Computers
ISBN : 9783709106464

Get Book

Artificial Neural Nets and Genetic Algorithms by David W. Pearson,Nigel C. Steele,Rudolf Albrecht Pdf

The 2003 edition of ICANNGA marks a milestone in this conference series, because it is the tenth year of its existence. The series began in 1993 with the inaugural conference at Innsbruck in Austria. At that first conference, the organisers decided to organise a similar scientific meeting every two years. As a result, conferences were organised at Ales in France (1995), Norwich in England (1997), Portoroz in Slovenia (1999) and Prague in the Czech Republic (2001). It is a great honour that the conference is taking place in France for the second time. Each edition of ICANNGA has been special and had its own character. Not only that, participants have been able to sample the life and local culture in five different European coun tries. Originally limited to neural networks and genetic algorithms the conference has broadened its outlook over the past ten years and now includes papers on soft computing and artificial intelligence in general. This is one of the reasons why the reader will find papers on fuzzy logic and various other topics not directly related to neural networks or genetic algorithms included in these proceedings. We have, however, kept the same name, "International Conference on Artificial Neural Networks and Genetic Algorithms". All of the papers were sorted into one of six principal categories: neural network theory, neural network applications, genetic algorithm and evolutionary computation theory, genetic algorithm and evolutionary computation applications, fuzzy and soft computing theory, fuzzy and soft computing applications.

Evolutionary Machine Learning Techniques

Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah
Evolutionary Machine Learning Techniques Book PDF

Author : Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah
Publisher : Springer Nature
Page : 286 pages
File Size : 53,7 Mb
Release : 2019-11-11
Category : Technology & Engineering
ISBN : 9789813299900

Get Book

Evolutionary Machine Learning Techniques by Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah Pdf

This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.

Artificial Neural Nets and Genetic Algorithms

David W. Pearson,Nigel C. Steele,Rudolf F. Albrecht
Artificial Neural Nets and Genetic Algorithms Book PDF

Author : David W. Pearson,Nigel C. Steele,Rudolf F. Albrecht
Publisher : Springer Science & Business Media
Page : 542 pages
File Size : 55,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783709175354

Get Book

Artificial Neural Nets and Genetic Algorithms by David W. Pearson,Nigel C. Steele,Rudolf F. Albrecht Pdf

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.

Evolutionary Algorithms in Intelligent Systems

Alfredo Milani,Arturo Carpi,Valentina Poggioni
Evolutionary Algorithms in Intelligent Systems Book PDF

Author : Alfredo Milani,Arturo Carpi,Valentina Poggioni
Publisher : MDPI
Page : 144 pages
File Size : 53,7 Mb
Release : 2020-12-07
Category : Technology & Engineering
ISBN : 9783039436118

Get Book

Evolutionary Algorithms in Intelligent Systems by Alfredo Milani,Arturo Carpi,Valentina Poggioni Pdf

Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.

An Introduction to Genetic Algorithms

Melanie Mitchell
An Introduction to Genetic Algorithms Book PDF

Author : Melanie Mitchell
Publisher : MIT Press
Page : 226 pages
File Size : 44,7 Mb
Release : 1998-03-02
Category : Computers
ISBN : 0262631857

Get Book

An Introduction to Genetic Algorithms by Melanie Mitchell Pdf

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Introduction to Evolutionary Algorithms

Xinjie Yu,Mitsuo Gen
Introduction to Evolutionary Algorithms Book PDF

Author : Xinjie Yu,Mitsuo Gen
Publisher : Springer Science & Business Media
Page : 422 pages
File Size : 45,8 Mb
Release : 2010-06-10
Category : Computers
ISBN : 9781849961295

Get Book

Introduction to Evolutionary Algorithms by Xinjie Yu,Mitsuo Gen Pdf

Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.

Artificial Neural Nets and Genetic Algorithms

Rudolf F. Albrecht,Colin R. Reeves,Nigel C. Steele
Artificial Neural Nets and Genetic Algorithms Book PDF

Author : Rudolf F. Albrecht,Colin R. Reeves,Nigel C. Steele
Publisher : Springer Science & Business Media
Page : 752 pages
File Size : 49,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783709175330

Get Book

Artificial Neural Nets and Genetic Algorithms by Rudolf F. Albrecht,Colin R. Reeves,Nigel C. Steele Pdf

Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are the subjects of contributions to this volume. There are contributions reporting theoretical developments in the design of neural networks, and in the management of their learning. In a number of contributions, applications to speech recognition tasks, control of industrial processes as well as to credit scoring, and so on, are reflected. Regarding genetic algorithms, several methodological papers consider how genetic algorithms can be improved using an experimental approach, as well as by hybridizing with other useful techniques such as tabu search. The closely related area of classifier systems also receives a significant amount of coverage, aiming at better ways for their implementation. Further, while there are many contributions which explore ways in which genetic algorithms can be applied to real problems, nearly all involve some understanding of the context in order to apply the genetic algorithm paradigm more successfully. That this can indeed be done is evidenced by the range of applications covered in this volume.

Artificial Neural Nets and Genetic Algorithms

George D. Smith,Nigel C. Steele,Rudolf F. Albrecht
Artificial Neural Nets and Genetic Algorithms Book PDF

Author : George D. Smith,Nigel C. Steele,Rudolf F. Albrecht
Publisher : Springer Science & Business Media
Page : 654 pages
File Size : 48,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783709164921

Get Book

Artificial Neural Nets and Genetic Algorithms by George D. Smith,Nigel C. Steele,Rudolf F. Albrecht Pdf

This is the third in a series of conferences devoted primarily to the theory and applications of artificial neural networks and genetic algorithms. The first such event was held in Innsbruck, Austria, in April 1993, the second in Ales, France, in April 1995. We are pleased to host the 1997 event in the mediaeval city of Norwich, England, and to carryon the fine tradition set by its predecessors of providing a relaxed and stimulating environment for both established and emerging researchers working in these and other, related fields. This series of conferences is unique in recognising the relation between the two main themes of artificial neural networks and genetic algorithms, each having its origin in a natural process fundamental to life on earth, and each now well established as a paradigm fundamental to continuing technological development through the solution of complex, industrial, commercial and financial problems. This is well illustrated in this volume by the numerous applications of both paradigms to new and challenging problems. The third key theme of the series, therefore, is the integration of both technologies, either through the use of the genetic algorithm to construct the most effective network architecture for the problem in hand, or, more recently, the use of neural networks as approximate fitness functions for a genetic algorithm searching for good solutions in an 'incomplete' solution space, i.e. one for which the fitness is not easily established for every possible solution instance.