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Evolutionary Approach To Machine Learning And Deep Neural Networks

Evolutionary Approach to Machine Learning and Deep Neural Networks PDF
Author: Hitoshi Iba
Publisher: Springer
ISBN: 9811302006
Size: 16.68 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 245
View: 6278

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Evolutionary Approach To Machine Learning And Deep Neural Networks

by Hitoshi Iba, Evolutionary Approach To Machine Learning And Deep Neural Networks Books available in PDF, EPUB, Mobi Format. Download Evolutionary Approach To Machine Learning And Deep Neural Networks books, 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

Deep Neural Evolution PDF
Author: Hitoshi Iba
Publisher: Springer Nature
ISBN: 9811536856
Size: 42.38 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 438
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Deep Neural Evolution

by Hitoshi Iba, Deep Neural Evolution Books available in PDF, EPUB, Mobi Format. Download Deep Neural Evolution books, 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 Machine Learning Techniques

Evolutionary Machine Learning Techniques PDF
Author: Seyedali Mirjalili
Publisher: Springer Nature
ISBN: 9813299908
Size: 27.92 MB
Format: PDF, ePub, Docs
Category : Technology & Engineering
Languages : en
Pages : 286
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Evolutionary Machine Learning Techniques

by Seyedali Mirjalili, Evolutionary Machine Learning Techniques Books available in PDF, EPUB, Mobi Format. Download Evolutionary Machine Learning Techniques books, 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.




Hands On Neuroevolution With Python

Hands On Neuroevolution with Python PDF
Author: Iaroslav Omelianenko
Publisher: Packt Publishing Ltd
ISBN: 1838822003
Size: 19.38 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 368
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Hands On Neuroevolution With Python

by Iaroslav Omelianenko, Hands On Neuroevolution With Python Books available in PDF, EPUB, Mobi Format. Download Hands On Neuroevolution With Python books, Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution Key Features Implement neuroevolution algorithms to improve the performance of neural network architectures Understand evolutionary algorithms and neuroevolution methods with real-world examples Learn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulations Book Description Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments. What you will learn Discover the most popular neuroevolution algorithms – NEAT, HyperNEAT, and ES-HyperNEAT Explore how to implement neuroevolution-based algorithms in Python Get up to speed with advanced visualization tools to examine evolved neural network graphs Understand how to examine the results of experiments and analyze algorithm performance Delve into neuroevolution techniques to improve the performance of existing methods Apply deep neuroevolution to develop agents for playing Atari games Who this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.




Advancing Technology Industrialization Through Intelligent Software Methodologies Tools And Techniques

Advancing Technology Industrialization Through Intelligent Software Methodologies  Tools and Techniques PDF
Author: H. Fujita
Publisher: IOS Press
ISBN: 1643680137
Size: 69.51 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 768
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Advancing Technology Industrialization Through Intelligent Software Methodologies Tools And Techniques

by H. Fujita, Advancing Technology Industrialization Through Intelligent Software Methodologies Tools And Techniques Books available in PDF, EPUB, Mobi Format. Download Advancing Technology Industrialization Through Intelligent Software Methodologies Tools And Techniques books, Software has become ever more crucial as an enabler, from daily routines to important national decisions. But from time to time, as society adapts to frequent and rapid changes in technology, software development fails to come up to expectations due to issues with efficiency, reliability and security, and with the robustness of methodologies, tools and techniques not keeping pace with the rapidly evolving market. This book presents the proceedings of SoMeT_19, the 18th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, held in Kuching, Malaysia, from 23–25 September 2019. The book explores new trends and theories that highlight the direction and development of software methodologies, tools and techniques, and aims to capture the essence of a new state of the art in software science and its supporting technology, and to identify the challenges that such a technology will have to master. The book also investigates other comparable theories and practices in software science, including emerging technologies, from their computational foundations in terms of models, methodologies, and tools. The 56 papers included here are divided into 5 chapters: Intelligent software systems design and techniques in software engineering; Machine learning techniques for software systems; Requirements engineering, software design and development techniques; Software methodologies, tools and techniques for industry; and Knowledge science and intelligent computing. This comprehensive overview of information systems and research projects will be invaluable to all those whose work involves the assessment and solution of real-world software problems.




An Introduction To Genetic Algorithms

An Introduction to Genetic Algorithms PDF
Author: Melanie Mitchell
Publisher: MIT Press
ISBN: 9780262631853
Size: 79.45 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 209
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An Introduction To Genetic Algorithms

by Melanie Mitchell, An Introduction To Genetic Algorithms Books available in PDF, EPUB, Mobi Format. Download An Introduction To Genetic Algorithms books, Genetic algorithms : an overview - Genetic algorithms in problem solving - Genetic algorithms in scientific models - Theoretical foundations of genetic algorithms - Implementing a genetic algorithm.




Hands On Genetic Algorithms With Python

Hands On Genetic Algorithms with Python PDF
Author: Eyal Wirsansky
Publisher: Packt Publishing Ltd
ISBN: 1838559183
Size: 39.50 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 346
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Hands On Genetic Algorithms With Python

by Eyal Wirsansky, Hands On Genetic Algorithms With Python Books available in PDF, EPUB, Mobi Format. Download Hands On Genetic Algorithms With Python books, Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What you will learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.




Advances In Neural Computation Machine Learning And Cognitive Research Iii

Advances in Neural Computation  Machine Learning  and Cognitive Research III PDF
Author: Boris Kryzhanovsky
Publisher: Springer Nature
ISBN: 3030304256
Size: 55.11 MB
Format: PDF
Category : Technology & Engineering
Languages : en
Pages : 428
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Advances In Neural Computation Machine Learning And Cognitive Research Iii

by Boris Kryzhanovsky, Advances In Neural Computation Machine Learning And Cognitive Research Iii Books available in PDF, EPUB, Mobi Format. Download Advances In Neural Computation Machine Learning And Cognitive Research Iii books, This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXI International Conference on Neuroinformatics, held on October 7-11, 2019, in Dolgoprudny, a town in Moscow region, Russia.




Deep Learning For Image Processing Applications

Deep Learning for Image Processing Applications PDF
Author: D.J. Hemanth
Publisher: IOS Press
ISBN: 1614998221
Size: 26.99 MB
Format: PDF, Docs
Category : COMPUTERS
Languages : en
Pages : 284
View: 7235

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Deep Learning For Image Processing Applications

by D.J. Hemanth, Deep Learning For Image Processing Applications Books available in PDF, EPUB, Mobi Format. Download Deep Learning For Image Processing Applications books, Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.