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Author: Hitoshi Iba
Publisher: Springer Nature
ISBN: 9811536856
Category : Computers
Languages : en
Pages : 437
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Book Description
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: Hitoshi Iba
Publisher: Springer Nature
ISBN: 9811536856
Category : Computers
Languages : en
Pages : 437
Get Book Here
Book Description
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: Hitoshi Iba
Publisher: Springer
ISBN: 9811302006
Category : Computers
Languages : en
Pages : 254
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Book Description
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: Seyedali Mirjalili
Publisher: Springer Nature
ISBN: 9813299908
Category : Technology & Engineering
Languages : en
Pages : 287
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Book Description
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: Seyedali Mirjalili
Publisher: Springer
ISBN: 3319930257
Category : Technology & Engineering
Languages : en
Pages : 164
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Book Description
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.
Author: Michael Lanham
Publisher: Simon and Schuster
ISBN: 1617299529
Category : Computers
Languages : en
Pages : 358
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Book Description
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. 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. Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time you’ve finished reading Evolutionary Deep Learning, you’ll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Author: Micheal Lanham
Publisher: Simon and Schuster
ISBN: 1638352321
Category : Computers
Languages : en
Pages : 599
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Book Description
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: Yaochu Jin
Publisher: Springer Nature
ISBN: 3030746402
Category : Computers
Languages : en
Pages : 393
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Book Description
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: Alain Petrowski
Publisher: John Wiley & Sons
ISBN: 1848218044
Category : Computers
Languages : en
Pages : 260
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Book Description
Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.
Author: Kenneth A. De Jong
Publisher: MIT Press
ISBN: 0262041944
Category : Computers
Languages : en
Pages : 267
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Book Description
This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.
Author: Hitoshi Iba
Publisher: CRC Press
ISBN: 1000579905
Category : Computers
Languages : en
Pages : 288
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Book Description
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.
