Evolutionary Approach To Machine Learning And Deep Neural Networks

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Evolutionary Approach to Machine Learning and Deep Neural Networks

Author : Hitoshi Iba
Publisher : Springer
ISBN 13 : 9811302006
Total Pages : 254 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Evolutionary Approach to Machine Learning and Deep Neural Networks by : Hitoshi Iba

Download or read book Evolutionary Approach to Machine Learning and Deep Neural Networks written by Hitoshi Iba and published by Springer. This book was released on 2018-06-15 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Neural Evolution

Author : Hitoshi Iba
Publisher : Springer Nature
ISBN 13 : 9811536856
Total Pages : 437 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Deep Neural Evolution by : Hitoshi Iba

Download or read book Deep Neural Evolution written by Hitoshi Iba and published by Springer Nature. This book was released on 2020-05-20 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Handbook of Evolutionary Machine Learning

Author : Wolfgang Banzhaf
Publisher : Springer Nature
ISBN 13 : 9819938147
Total Pages : 764 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Handbook of Evolutionary Machine Learning by : Wolfgang Banzhaf

Download or read book Handbook of Evolutionary Machine Learning written by Wolfgang Banzhaf and published by Springer Nature. This book was released on 2023-11-01 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.

Evolutionary Algorithms and Neural Networks

Author : Seyedali Mirjalili
Publisher : Springer
ISBN 13 : 3319930257
Total Pages : 164 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Evolutionary Algorithms and Neural Networks by : Seyedali Mirjalili

Download or read book Evolutionary Algorithms and Neural Networks written by Seyedali Mirjalili and published by Springer. This book was released on 2018-06-26 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Deep Learning

Author : Micheal Lanham
Publisher : Simon and Schuster
ISBN 13 : 1638352321
Total Pages : 599 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Evolutionary Deep Learning by : Micheal Lanham

Download or read book Evolutionary Deep Learning written by Micheal Lanham and published by Simon and Schuster. This book was released on 2023-10-03 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. In Evolutionary Deep Learning you will learn how to: Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization Use unsupervised learning with a deep learning autoencoder to regenerate sample data Understand the basics of reinforcement learning and the Q-Learning equation Apply Q-Learning to deep learning to produce deep reinforcement learning Optimize the loss function and network architecture of unsupervised autoencoders Make an evolutionary agent that can play an OpenAI Gym game Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture. About the technology Deep learning meets evolutionary biology in this incredible book. Explore how biology-inspired algorithms and intuitions amplify the power of neural networks to solve tricky search, optimization, and control problems. Relevant, practical, and extremely interesting examples demonstrate how ancient lessons from the natural world are shaping the cutting edge of data science. About the book Evolutionary Deep Learning introduces evolutionary computation (EC) and gives you a toolbox of techniques you can apply throughout the deep learning pipeline. Discover genetic algorithms and EC approaches to network topology, generative modeling, reinforcement learning, and more! Interactive Colab notebooks give you an opportunity to experiment as you explore. What's inside Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters Apply Q-Learning to deep learning to produce deep reinforcement learning Optimize the loss function and network architecture of unsupervised autoencoders Make an evolutionary agent that can play an OpenAI Gym game About the reader For data scientists who know Python. About the author Micheal Lanham is a proven software and tech innovator with over 20 years of experience. Table of Contents PART 1 - GETTING STARTED 1 Introducing evolutionary deep learning 2 Introducing evolutionary computation 3 Introducing genetic algorithms with DEAP 4 More evolutionary computation with DEAP PART 2 - OPTIMIZING DEEP LEARNING 5 Automating hyperparameter optimization 6 Neuroevolution optimization 7 Evolutionary convolutional neural networks PART 3 - ADVANCED APPLICATIONS 8 Evolving autoencoders 9 Generative deep learning and evolution 10 NEAT: NeuroEvolution of Augmenting Topologies 11 Evolutionary learning with NEAT 12 Evolutionary machine learning and beyond

Evolutionary Machine Learning Techniques

Author : Seyedali Mirjalili
Publisher : Springer Nature
ISBN 13 : 9813299908
Total Pages : 286 pages
Book Rating : 4.8/5 (132 download)

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Book Synopsis Evolutionary Machine Learning Techniques by : Seyedali Mirjalili

Download or read book Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and published by Springer Nature. This book was released on 2019-11-11 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Evolutionary Artificial Intelligence

Author : David Asirvatham
Publisher : Springer Nature
ISBN 13 : 9819984386
Total Pages : 563 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Evolutionary Artificial Intelligence by : David Asirvatham

Download or read book Evolutionary Artificial Intelligence written by David Asirvatham and published by Springer Nature. This book was released on with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Neural Nets and Genetic Algorithms

Author : Rudolf F. Albrecht
Publisher : Springer Science & Business Media
ISBN 13 : 370917533X
Total Pages : 752 pages
Book Rating : 4.7/5 (91 download)

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Book Synopsis Artificial Neural Nets and Genetic Algorithms by : Rudolf F. Albrecht

Download or read book Artificial Neural Nets and Genetic Algorithms written by Rudolf F. Albrecht and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Data-Driven Evolutionary Optimization

Author : Yaochu Jin
Publisher : Springer Nature
ISBN 13 : 3030746402
Total Pages : 393 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Data-Driven Evolutionary Optimization by : Yaochu Jin

Download or read book Data-Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

Advanced Machine Learning with Evolutionary and Metaheuristic Techniques

Author : Jayaraman Valadi
Publisher : Springer Nature
ISBN 13 : 9819997186
Total Pages : 365 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Advanced Machine Learning with Evolutionary and Metaheuristic Techniques by : Jayaraman Valadi

Download or read book Advanced Machine Learning with Evolutionary and Metaheuristic Techniques written by Jayaraman Valadi and published by Springer Nature. This book was released on with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Implementations and Applications of Machine Learning

Author : Saad Subair
Publisher : Springer Nature
ISBN 13 : 3030378306
Total Pages : 288 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Implementations and Applications of Machine Learning by : Saad Subair

Download or read book Implementations and Applications of Machine Learning written by Saad Subair and published by Springer Nature. This book was released on 2020-04-23 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides step-by-step explanations of successful implementations and practical applications of machine learning. The book’s GitHub page contains software codes to assist readers in adapting materials and methods for their own use. A wide variety of applications are discussed, including wireless mesh network and power systems optimization; computer vision; image and facial recognition; protein prediction; data mining; and data discovery. Numerous state-of-the-art machine learning techniques are employed (with detailed explanations), including biologically-inspired optimization (genetic and other evolutionary algorithms, swarm intelligence); Viola Jones face detection; Gaussian mixture modeling; support vector machines; deep convolutional neural networks with performance enhancement techniques (including network design, learning rate optimization, data augmentation, transfer learning); spiking neural networks and timing dependent plasticity; frequent itemset mining; binary classification; and dynamic programming. This book provides valuable information on effective, cutting-edge techniques, and approaches for students, researchers, practitioners, and teachers in the field of machine learning.

Evolutionary Computation

Author : Xin Yao
Publisher : World Scientific
ISBN 13 : 9789810223069
Total Pages : 384 pages
Book Rating : 4.2/5 (23 download)

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Book Synopsis Evolutionary Computation by : Xin Yao

Download or read book Evolutionary Computation written by Xin Yao and published by World Scientific. This book was released on 1999 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.

An Evolutionary Approach to Designing Neural Networks

Author : Aviv Bergman
Publisher :
ISBN 13 :
Total Pages : 51 pages
Book Rating : 4.:/5 (399 download)

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Book Synopsis An Evolutionary Approach to Designing Neural Networks by : Aviv Bergman

Download or read book An Evolutionary Approach to Designing Neural Networks written by Aviv Bergman and published by . This book was released on 1991 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Intelligence in the Age of Neural Networks and Brain Computing

Author : Robert Kozma
Publisher : Academic Press
ISBN 13 : 0323958168
Total Pages : 398 pages
Book Rating : 4.3/5 (239 download)

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Book Synopsis Artificial Intelligence in the Age of Neural Networks and Brain Computing by : Robert Kozma

Download or read book Artificial Intelligence in the Age of Neural Networks and Brain Computing written by Robert Kozma and published by Academic Press. This book was released on 2023-10-27 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Automatic Generation of Neural Network Architecture Using Evolutionary Computation

Author : E. Vonk
Publisher : World Scientific
ISBN 13 : 9789810231064
Total Pages : 196 pages
Book Rating : 4.2/5 (31 download)

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Book Synopsis Automatic Generation of Neural Network Architecture Using Evolutionary Computation by : E. Vonk

Download or read book Automatic Generation of Neural Network Architecture Using Evolutionary Computation written by E. Vonk and published by World Scientific. This book was released on 1997 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning Deep Learning

Author : Magnus Ekman
Publisher : Addison-Wesley Professional
ISBN 13 : 0137470290
Total Pages : 1106 pages
Book Rating : 4.1/5 (374 download)

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Book Synopsis Learning Deep Learning by : Magnus Ekman

Download or read book Learning Deep Learning written by Magnus Ekman and published by Addison-Wesley Professional. This book was released on 2021-07-19 with total page 1106 pages. Available in PDF, EPUB and Kindle. Book excerpt: NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances

Author : Yanan Sun
Publisher : Springer Nature
ISBN 13 : 3031168682
Total Pages : 335 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances by : Yanan Sun

Download or read book Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances written by Yanan Sun and published by Springer Nature. This book was released on 2022-11-08 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.