Evolutionary Approach To Machine Learning And Deep Neural Networks

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Evolutionary Approach To Machine Learning And Deep Neural Networks


Evolutionary Approach To Machine Learning And Deep Neural Networks
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Evolutionary Approach To Machine Learning And Deep Neural Networks


Evolutionary Approach To Machine Learning And Deep Neural Networks
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Author : Hitoshi Iba
language : en
Publisher: Springer
Release Date : 2018-06-15

Evolutionary Approach To Machine Learning And Deep Neural Networks written by Hitoshi Iba and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-15 with Computers categories.


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
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Author : Hitoshi Iba
language : en
Publisher: Springer Nature
Release Date : 2020-05-20

Deep Neural Evolution written by Hitoshi Iba and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-20 with Computers categories.


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
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Author : Seyedali Mirjalili
language : en
Publisher: Springer Nature
Release Date : 2019-11-11

Evolutionary Machine Learning Techniques written by Seyedali Mirjalili and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-11 with Technology & Engineering categories.


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.

Genetic Programming For Image Classification


Genetic Programming For Image Classification
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Author : Ying Bi
language : en
Publisher: Springer Nature
Release Date : 2021

Genetic Programming For Image Classification written by Ying Bi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Electronic books categories.




Ai And Swarm


Ai And Swarm
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Author : Hitoshi Iba
language : en
Publisher: CRC Press
Release Date : 2019-09-12

Ai And Swarm written by Hitoshi Iba and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-12 with Computers categories.


This book provides theoretical and practical knowledge on AI and swarm intelligence. It provides a methodology for EA (evolutionary algorithm)-based approach for complex adaptive systems with the integration of several meta-heuristics, e.g., ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), and PSO (Particle Swarm Optimization), etc. These developments contribute towards better problem-solving methodologies in AI. The book also covers emerging uses of swarm intelligence in applications such as complex adaptive systems, reaction-diffusion computing, and diffusion-limited aggregation, etc. Another emphasis is its real-world applications. We give empirical examples from real-world problems and show that the proposed approaches are successful when addressing tasks from such areas as swarm robotics, silicon traffics, image understanding, Vornoi diagrams, queuing theory, and slime intelligence, etc. Each chapter begins with the background of the problem followed by the current state-of-the-art techniques of the field, and ends with a detailed discussion. In addition, the simulators, based on optimizers such as PSO and ABC complex adaptive system simulation, are described in detail. These simulators, as well as some source codes, are available online on the author’s website for the benefit of readers interested in getting some hands-on experience of the subject. The concepts presented in this book aim to promote and facilitate the effective research in swarm intelligence approaches in both theory and practice. This book would also be of value to other readers because it covers interdisciplinary research topics that encompass problem-solving tasks in AI, complex adaptive systems, and meta-heuristics.

Artificial Intelligence For Business Optimization


Artificial Intelligence For Business Optimization
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Author : Bhuvan Unhelkar
language : en
Publisher: CRC Press
Release Date : 2021-08-10

Artificial Intelligence For Business Optimization written by Bhuvan Unhelkar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-10 with Computers categories.


This is primarily a business book that discusses the research and associated practical application of artificial intelligence (AI) and machine learning (ML) in order to achieve business optimization (BO). AI comprises a wide range of technologies, databases, algorithms, and devices. This book aims for a holistic approach to AI by focusing on developing business strategies that will not only automate but also optimize business functions, processes, and people’s behaviors. Artificial Intelligence for Business Optimization: Research and Applications explores AI and ML from a business viewpoint with the key purpose of enhancing customer value. It applies research methods and fundamentals from a practitioner’s viewpoint and incorporates discussions around risks and changes associated with the utilization of AI in business. Furthermore, governance risks, privacy, and security are also addressed in this book to ensure compliance with AI/ML applications. Readers should find direct and practical applications of the discussions in this book quite useful in their work environment. Researchers will find many ideas to further explore the applications of AI to business.

Intelligent Data Engineering And Automated Learning Ideal 2018


Intelligent Data Engineering And Automated Learning Ideal 2018
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Author : Hujun Yin
language : en
Publisher: Springer
Release Date : 2018-11-08

Intelligent Data Engineering And Automated Learning Ideal 2018 written by Hujun Yin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-08 with Computers categories.


This two-volume set LNCS 11314 and 11315 constitutes the thoroughly refereed conference proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, held in Madrid, Spain, in November 2018. The 125 full papers presented were carefully reviewed and selected from 204 submissions. These papers provided a timely sample of the latest advances in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.