Deep Neural Evolution - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Neural Evolution write by Hitoshi Iba. This book was released on 2020-05-20. Deep Neural Evolution available in PDF, EPUB and Kindle. 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
Evolutionary Approach to Machine Learning and Deep Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Evolutionary Approach to Machine Learning and Deep Neural Networks write by Hitoshi Iba. This book was released on 2018-06-15. Evolutionary Approach to Machine Learning and Deep Neural Networks available in PDF, EPUB and Kindle. 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 Algorithms and Neural Networks
Evolutionary Algorithms and Neural Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Evolutionary Algorithms and Neural Networks write by Seyedali Mirjalili. This book was released on 2018-06-26. Evolutionary Algorithms and Neural Networks available in PDF, EPUB and Kindle. 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.
Neuroevolution
Neuroevolution - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Neuroevolution write by Fouad Sabry. This book was released on 2023-06-21. Neuroevolution available in PDF, EPUB and Kindle. 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.
Artificial Intelligence in the Age of Neural Networks and Brain Computing
Artificial Intelligence in the Age of Neural Networks and Brain Computing - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Intelligence in the Age of Neural Networks and Brain Computing write by Robert Kozma. This book was released on 2023-10-27. Artificial Intelligence in the Age of Neural Networks and Brain Computing available in PDF, EPUB and Kindle. 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
