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Evolutionary Deep Learning

2023-07-18 Computers
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Release : 2023-07-18
Genre : Computers
Kind : eBook
Book Rating : 529/5 ( reviews)

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Book Synopsis Evolutionary Deep Learning by : Michael Lanham

Download or read book Evolutionary Deep Learning written by Michael Lanham. This book was released on 2023-07-18. 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. 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.

Deep Neural Evolution

2020-05-20 Computers
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Release : 2020-05-20
Genre : Computers
Kind : eBook
Book Rating : 856/5 ( reviews)

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Book Synopsis Deep Neural Evolution by : Hitoshi Iba

Download or read book Deep Neural Evolution written by Hitoshi Iba. This book was released on 2020-05-20. 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.

Evolutionary Approach to Machine Learning and Deep Neural Networks

2018-06-15 Computers
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Release : 2018-06-15
Genre : Computers
Kind : eBook
Book Rating : 006/5 ( reviews)

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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. This book was released on 2018-06-15. 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.

Evolutionary Machine Learning Techniques

2019-11-11 Technology & Engineering
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Release : 2019-11-11
Genre : Technology & Engineering
Kind : eBook
Book Rating : 908/5 ( reviews)

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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. This book was released on 2019-11-11. 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 Algorithms and Neural Networks

2018-06-26 Technology & Engineering
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Release : 2018-06-26
Genre : Technology & Engineering
Kind : eBook
Book Rating : 257/5 ( reviews)

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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. This book was released on 2018-06-26. 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.

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