Machine Learning For Evolution Strategies
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Machine Learning For Evolution Strategies
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Author : Oliver Kramer
language : en
Publisher: Springer
Release Date : 2016-05-25
Machine Learning For Evolution Strategies written by Oliver Kramer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-25 with Technology & Engineering categories.
This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.
Handbook Of Evolutionary Machine Learning
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Author : Wolfgang Banzhaf
language : en
Publisher: Springer Nature
Release Date : 2023-11-01
Handbook Of Evolutionary Machine Learning written by Wolfgang Banzhaf and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-01 with Computers categories.
This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.
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 Computation
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Author : David B. Fogel
language : en
Publisher: John Wiley & Sons
Release Date : 2006-01-03
Evolutionary Computation written by David B. Fogel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-03 with Technology & Engineering categories.
This Third Edition provides the latest tools and techniques thatenable computers to learn The Third Edition of this internationally acclaimed publicationprovides the latest theory and techniques for using simulatedevolution to achieve machine intelligence. As a leading advocatefor evolutionary computation, the author has successfullychallenged the traditional notion of artificial intelligence, whichessentially programs human knowledge fact by fact, but does nothave the capacity to learn or adapt as evolutionary computationdoes. Readers gain an understanding of the history of evolutionarycomputation, which provides a foundation for the author's thoroughpresentation of the latest theories shaping current research.Balancing theory with practice, the author provides readers withthe skills they need to apply evolutionary algorithms that cansolve many of today's intransigent problems by adapting to newchallenges and learning from experience. Several examples areprovided that demonstrate how these evolutionary algorithms learnto solve problems. In particular, the author provides a detailedexample of how an algorithm is used to evolve strategies forplaying chess and checkers. As readers progress through the publication, they gain anincreasing appreciation and understanding of the relationshipbetween learning and intelligence. Readers familiar with theprevious editions will discover much new and revised material thatbrings the publication thoroughly up to date with the latestresearch, including the latest theories and empirical properties ofevolutionary computation. The Third Edition also features new knowledge-building aids.Readers will find a host of new and revised examples. New questionsat the end of each chapter enable readers to test their knowledge.Intriguing assignments that prepare readers to manage challenges inindustry and research have been added to the end of each chapter aswell. This is a must-have reference for professionals in computer andelectrical engineering; it provides them with the very latesttechniques and applications in machine intelligence. With itsquestion sets and assignments, the publication is also recommendedas a graduate-level textbook.
Advances In Evolutionary Computing
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Author : Ashish Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Advances In Evolutionary Computing written by Ashish Ghosh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
This book provides a collection of fourty articles containing new material on both theoretical aspects of Evolutionary Computing (EC), and demonstrating the usefulness/success of it for various kinds of large-scale real world problems. Around 23 articles deal with various theoretical aspects of EC and 17 articles demonstrate the success of EC methodologies. These articles are written by leading experts of the field from different countries all over the world.
Evolutionary Algorithms For Solving Multi Objective Problems
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Author : Carlos Coello Coello
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Evolutionary Algorithms For Solving Multi Objective Problems written by Carlos Coello Coello and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Computers categories.
Researchers and practitioners alike are increasingly turning to search, op timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solv ing problems and inventing new hardware and software that rival human designs. The Kluwer Series on Genetic Algorithms and Evolutionary Computation pub lishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implemen tation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also pub lishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described. This encyclopedic volume on the use of the algorithms of genetic and evolu tionary computation for the solution of multi-objective problems is a landmark addition to the literature that comes just in the nick of time. Multi-objective evolutionary algorithms (MOEAs) are receiving increasing and unprecedented attention. Researchers and practitioners are finding an irresistible match be tween the popUlation available in most genetic and evolutionary algorithms and the need in multi-objective problems to approximate the Pareto trade-off curve or surface.
The Theory Of Evolution Strategies
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Author : Hans-Georg Beyer
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
The Theory Of Evolution Strategies written by Hans-Georg Beyer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Computers categories.
Evolutionary algorithms, such as evolution strategies, genetic algorithms, or evolutionary programming, have found broad acceptance in the last ten years. In contrast to its broad propagation, theoretical analysis in this subject has not progressed as much. This monograph provides the framework and the first steps toward the theoretical analysis of Evolution Strategies (ES). The main emphasis is deriving a qualitative understanding of why and how these ES algorithms work.
Evolutionary Computation
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Author : Xin Yao
language : en
Publisher: World Scientific
Release Date : 1999
Evolutionary Computation written by Xin Yao and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Science categories.
Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
Evolutionary Algorithms In Theory And Practice
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Author : Thomas Bäck
language : en
Publisher: Oxford University Press, USA
Release Date : 1996
Evolutionary Algorithms In Theory And Practice written by Thomas Bäck and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
A comparison of evolutionary algorithms. Organic evolution and problem solving. Biological background. Evolutionary algorithms and artificial intelligence. Evolutionary algorithms and global optimization. Early approaches. Specific evolutionary algorithms. Evolution strategies. Evolutionary programming. Genetic algorithms. Artificial landscapes. An empirical comparison. Extending genetic algorithms. Selection. Selection mechanisms. Experimental investigation of selection. Mutation. Simplified genetic algorithms. An experiment in meta-evolution. Summary and outlook. Data for the fletcher-powell function. Data from selection experiments. Software. The multiprocessor environment; mathematical symbols.
Evolutionary Computation
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Author : David B. Fogel
language : en
Publisher: Wiley-IEEE Press
Release Date : 2000
Evolutionary Computation written by David B. Fogel and has been published by Wiley-IEEE Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Science categories.
"In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research. Fogel carefully reviews the "no free lunch theorem" and discusses new theoretical findings that challenge some of the mathematical foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence."