Evolutionary Learning Advances In Theories And Algorithms
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Evolutionary Learning Advances In Theories And Algorithms
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Author : Zhi-Hua Zhou
language : en
Publisher: Springer
Release Date : 2019-05-22
Evolutionary Learning Advances In Theories And Algorithms written by Zhi-Hua Zhou and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-22 with Computers categories.
Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.
Recent Advances In Simulated Evolution And Learning
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Author : K. C. Tan
language : en
Publisher: World Scientific
Release Date : 2004
Recent Advances In Simulated Evolution And Learning written by K. C. Tan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.
Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances in the theories, algorithms, and applications of simulated evolution and learning techniques. It provides insights into different evolutionary computation techniques and their applications in domains such as scheduling, control and power, robotics, signal processing, and bioinformatics. The book will be of significant value to all postgraduates, research scientists and practitioners dealing with evolutionary computation or complex real-world problems. This book has been selected for coverage in: . OCo Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings). OCo CC Proceedings OCo Engineering & Physical Sciences. Sample Chapter(s). Chapter 1: Co-Evolutionary Learning in Strategic Environments (231 KB). Contents: Evolutionary Theory: Using Evolution to Learn User Preferences (S Ujjin & P J Bentley); Evolutionary Learning Strategies for Artificial Life Characters (M L Netto et al.); The Influence of Stochastic Quality Functions on Evolutionary Search (B Sendhoff et al.); A Real-Coded Cellular Genetic Algorithm Inspired by PredatorOCoPrey Interactions (X Li & S Sutherland); Automatic Modularization with Speciated Neural Network Ensemble (V R Khare & X Yao); Evolutionary Applications: Image Classification using Particle Swarm Optimization (M G Omran et al.); Evolution of Fuzzy Rule Based Controllers for Dynamic Environments (J Riley & V Ciesielski); A Genetic Algorithm for Joint Optimization of Spare Capacity and Delay in Self-Healing Network (S Kwong & H W Chong); Joint Attention in the Mimetic Context OCo What is a OC Mimetic SameOCO? (T Shiose et al.); Time Series Forecast with Elman Neural Networks and Genetic Algorithms (L X Xu et al.); and other articles. Readership: Upper level undergraduates, graduate students, academics, researchers and industrialists in artificial intelligence, evolutionary computation, fuzzy logic and neural networks."
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.
Advances In Evolutionary Algorithms
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Author : Chang Wook Ahn
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-02
Advances In Evolutionary Algorithms written by Chang Wook Ahn 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 2006-03-02 with Mathematics categories.
Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.
Advances In Evolutionary Computing For System Design
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Author : Vasile Palade
language : en
Publisher: Springer
Release Date : 2007-07-07
Advances In Evolutionary Computing For System Design written by Vasile Palade and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-07 with Computers categories.
Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book’s thirteen chapters cover a wide area of topics in evolutionary computing and applications, including an introduction to evolutionary computing in system design; evolutionary neuro-fuzzy systems; and evolution of fuzzy controllers. The book will be useful to researchers in intelligent systems with interest in evolutionary computing, as well as application engineers and system designers.
Evolutionary Computation Theory And Applications
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Author : Xin Yao
language : en
Publisher: World Scientific
Release Date : 1999-11-22
Evolutionary Computation Theory And Applications 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 1999-11-22 with Computers 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. Contents:Introduction (X Yao)Evolutionary Computation in Behavior Engineering (M Colombetti & M Dorigo)A General Method for Incremental Self-Improvement and Multi-Agent Learning (J Schmidhuber)Teacher: A Genetics-Based System for Learning and for Generalizing Heuristics (B W Wah & A Ieumwananonthachai)Automatic Discovery of Protein Motifs Using Genetic Programming (J R Koza & D Andre)The Role of Self Organization in Evolutionary Computations (A C Tsoi & J Shaw)Virus-Evolutionary Genetic Algorithm and Its Application to Traveling Salesman Problem (T Fukuda et al.)Hybrid Evolutionary Optimization Algorithm for Constrained Problems (J-H Kim & H Myung)CAM-BRAIN — The Evolutionary Engineering of a Billion Neuron Artificial Brain (H de Garis)An Evolutionary Approach to the N-Player Iterated Prisoner's Dilemma Game (X Yao & Darwen) Readership: Graduate students, practitioners and researchers in engineering and electronics and computer science. keywords:Genetic Algorithms;Evolutionary Computation;Evolutionary Algorithms;Genetic Programming;Evolutionary Robotics;Global Optimization;Evolutionary Games;Global Optimization;Machine Learning;Artificial Intelligence
Theory Of Evolutionary Computation
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Author : Benjamin Doerr
language : en
Publisher: Springer Nature
Release Date : 2019-11-20
Theory Of Evolutionary Computation written by Benjamin Doerr and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Computers categories.
This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influences performance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming. Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.
New Achievements In Evolutionary Computation
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Author : Peter Korosec
language : en
Publisher: BoD – Books on Demand
Release Date : 2010-02-01
New Achievements In Evolutionary Computation written by Peter Korosec and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-01 with Computers categories.
Evolutionary computation has been widely used in computer science for decades. Even though it started as far back as the 1960s with simulated evolution, the subject is still evolving. During this time, new metaheuristic optimization approaches, like evolutionary algorithms, genetic algorithms, swarm intelligence, etc., were being developed and new fields of usage in artificial intelligence, machine learning, combinatorial and numerical optimization, etc., were being explored. However, even with so much work done, novel research into new techniques and new areas of usage is far from over. This book presents some new theoretical as well as practical aspects of evolutionary computation. This book will be of great value to undergraduates, graduate students, researchers in computer science, and anyone else with an interest in learning about the latest developments in evolutionary computation.
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.
Evolutionary Algorithms In Theory And Practice
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Author : Thomas Back
language : en
Publisher: Oxford University Press
Release Date : 1996-01-11
Evolutionary Algorithms In Theory And Practice written by Thomas Back and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-01-11 with Computers categories.
This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.