Evolutionary Approach To Machine Learning And Deep Neural Networks

Author : Hitoshi Iba
Publisher : Springer
Page : 245 pages
File Size : 40,6 Mb
Release : 2018-06-15
Category : Computers
ISBN : 9789811302008

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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.

Author : Hitoshi Iba,Nasimul Noman
Publisher : Springer Nature
Page : 437 pages
File Size : 54,9 Mb
Release : 2020-05-20
Category : Computers
ISBN : 9789811536854

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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.

Author : Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang
Publisher : Springer Nature
Page : 764 pages
File Size : 51,6 Mb
Release : 2023-11-01
Category : Computers
ISBN : 9789819938148

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Handbook of Evolutionary Machine Learning by Wolfgang Banzhaf,Penousal Machado,Mengjie Zhang Pdf

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.

Author : Micheal Lanham
Publisher : Simon and Schuster
Page : 599 pages
File Size : 53,6 Mb
Release : 2023-10-03
Category : Computers
ISBN : 9781638352327

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Evolutionary Deep Learning by Micheal Lanham Pdf

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

Author : Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah
Publisher : Springer Nature
Page : 286 pages
File Size : 42,5 Mb
Release : 2019-11-11
Category : Technology & Engineering
ISBN : 9789813299900

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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.

Author : Hitoshi Iba
Publisher : CRC Press
Page : 288 pages
File Size : 47,9 Mb
Release : 2022-04-14
Category : Computers
ISBN : 9781000579901

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Swarm Intelligence and Deep Evolution by Hitoshi Iba Pdf

The book provides theoretical and practical knowledge about swarm intelligence and evolutionary computation. It describes the emerging trends in deep learning that involve the integration of swarm intelligence and evolutionary computation with deep learning, i.e., deep neuroevolution and deep swarms. The study reviews the research on network structures and hyperparameters in deep learning, and attracting attention as a new trend in AI. A part of the coverage of the book is based on the results of practical examples as well as various real-world applications. The future of AI, based on the ideas of swarm intelligence and evolution is also covered. The book is an introductory work for researchers. Approaches to the realization of AI and the emergence of intelligence are explained, with emphasis on evolution and learning. It is designed for beginners who do not have any knowledge of algorithms or biology, and explains the basics of neural networks and deep learning in an easy-to-understand manner. As a practical exercise in neuroevolution, the book shows how to learn to drive a racing car and a helicopter using MindRender. MindRender is an AI educational software that allows the readers to create and play with VR programs, and provides a variety of examples so that the readers will be able to create and understand AI.

Author : Yaochu Jin,Handing Wang,Chaoli Sun
Publisher : Springer Nature
Page : 393 pages
File Size : 50,9 Mb
Release : 2021-06-28
Category : Computers
ISBN : 9783030746407

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Data-Driven Evolutionary Optimization by Yaochu Jin,Handing Wang,Chaoli Sun Pdf

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.

Author : Jayaraman Valadi
Publisher : Springer Nature
Page : 365 pages
File Size : 45,5 Mb
Release : 2024-05-20
Category : Electronic
ISBN : 9789819997183

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Advanced Machine Learning with Evolutionary and Metaheuristic Techniques by Jayaraman Valadi Pdf

Author : Magnus Ekman
Publisher : Addison-Wesley Professional
Page : 1105 pages
File Size : 42,7 Mb
Release : 2021-07-19
Category : Computers
ISBN : 9780137470297

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Learning Deep Learning by Magnus Ekman Pdf

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.

Author : David Asirvatham
Publisher : Springer Nature
Page : 563 pages
File Size : 47,6 Mb
Release : 2024-05-20
Category : Electronic
ISBN : 9789819984381

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Evolutionary Artificial Intelligence by David Asirvatham Pdf

Author : Saad Subair,Christopher Thron
Publisher : Springer Nature
Page : 288 pages
File Size : 48,9 Mb
Release : 2020-04-23
Category : Technology & Engineering
ISBN : 9783030378301

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Implementations and Applications of Machine Learning by Saad Subair,Christopher Thron Pdf

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.

Author : Yanan Sun,Gary G. Yen,Mengjie Zhang
Publisher : Springer Nature
Page : 335 pages
File Size : 53,8 Mb
Release : 2022-11-08
Category : Technology & Engineering
ISBN : 9783031168680

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Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances by Yanan Sun,Gary G. Yen,Mengjie Zhang Pdf

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.

Author : R P Johnson,Lakhmi C Jain,E Vonk
Publisher : World Scientific
Page : 194 pages
File Size : 48,5 Mb
Release : 1997-10-31
Category : Computers
ISBN : 9789814497497

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Automatic Generation Of Neural Network Architecture Using Evolutionary Computation by R P Johnson,Lakhmi C Jain,E Vonk 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.

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
Page : 86 pages
File Size : 49,8 Mb
Release : 2023-06-21
Category : Computers
ISBN : PKEY:6610000468799

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Neuroevolution by Fouad Sabry Pdf

What Is Neuroevolution Neuroevolution, sometimes spelled neuro-evolution, is a form of artificial intelligence that generates artificial neural networks (ANN), parameters, and rules through the application of evolutionary algorithms. Neuroevolution is also spelled neuro-evolution. The most popular applications for this technique are found in evolutionary robotics, artificial life, and general game playing. The primary advantage is that neuroevolution may be applied to a wider variety of problems than supervised learning methods, which need a curriculum of accurate input-output pairings to function properly. Neuroevolution, on the other hand, needs nothing more than a measurement of how well a network performs at a given job. For instance, the result of a game can be easily measured even if the necessary strategies are not provided in the form of labeled examples. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm. It can be contrasted with conventional deep learning approaches, which make use of gradient descent on a neural network that possesses a fixed topology. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neuroevolution Chapter 2: Artificial neural network Chapter 3: Evolutionary algorithm Chapter 4: Genetic representation Chapter 5: Effective fitness Chapter 6: Neuroevolution of augmenting topologies Chapter 7: Recurrent neural network Chapter 8: Compositional pattern-producing network Chapter 9: HyperNEAT Chapter 10: Evolving intelligent system (II) Answering the public top questions about neuroevolution. (III) Real world examples for the usage of neuroevolution in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuroevolution. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

Author : Ying Bi,Bing Xue,Mengjie Zhang
Publisher : Springer Nature
Page : 279 pages
File Size : 43,6 Mb
Release : 2021-02-08
Category : Technology & Engineering
ISBN : 9783030659271

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Genetic Programming for Image Classification by Ying Bi,Bing Xue,Mengjie Zhang Pdf

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.