Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Genetic Algorithms Principles And Perspectives
Download Genetic Algorithms Principles And Perspectives full books in PDF, epub, and Kindle. Read online Genetic Algorithms Principles And Perspectives ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Genetic Algorithms: Principles and Perspectives by : Colin R. Reeves
Download or read book Genetic Algorithms: Principles and Perspectives written by Colin R. Reeves and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.
Book Synopsis Genetic Algorithms: Principles and Perspectives by : Colin Reeves
Download or read book Genetic Algorithms: Principles and Perspectives written by Colin Reeves and published by Springer. This book was released on 2013-05-05 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.
Book Synopsis Genetic Algorithms by : Kim-Fung Man
Download or read book Genetic Algorithms written by Kim-Fung Man and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive book gives a overview of the latest discussions in the application of genetic algorithms to solve engineering problems. Featuring real-world applications and an accompanying disk, giving the reader the opportunity to use an interactive genetic algorithms demonstration program.
Book Synopsis Introduction to Genetic Algorithms by : S.N. Sivanandam
Download or read book Introduction to Genetic Algorithms written by S.N. Sivanandam and published by Springer Science & Business Media. This book was released on 2007-10-24 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.
Book Synopsis Genetic Algorithm Essentials by : Oliver Kramer
Download or read book Genetic Algorithm Essentials written by Oliver Kramer and published by Springer. This book was released on 2017-01-07 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
Book Synopsis Proceedings of the First International Conference on Genetic Algorithms and their Applications by : John J. Grefenstette
Download or read book Proceedings of the First International Conference on Genetic Algorithms and their Applications written by John J. Grefenstette and published by Psychology Press. This book was released on 2014-01-02 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer solutions to many difficult problems in science and engineering require the use of automatic search methods that consider a large number of possible solutions to the given problems. This book describes recent advances in the theory and practice of one such search method, called Genetic Algorithms. Genetic algorithms are evolutionary search techniques based on principles derived from natural population genetics, and are currently being applied to a variety of difficult problems in science, engineering, and artificial intelligence.
Book Synopsis Genetic Algorithms and Genetic Programming by : Michael Affenzeller
Download or read book Genetic Algorithms and Genetic Programming written by Michael Affenzeller and published by CRC Press. This book was released on 2009-04-09 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for al
Book Synopsis Genetic Algorithms for Machine Learning by : John J. Grefenstette
Download or read book Genetic Algorithms for Machine Learning written by John J. Grefenstette and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.
Book Synopsis Foundations of Genetic Algorithms 3 by : L. Darrell Whitley
Download or read book Foundations of Genetic Algorithms 3 written by L. Darrell Whitley and published by Morgan Kaufmann Pub. This book was released on 1995 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Schema based analyses. Convergence and predictive models. Fitness landscapes and genetic operators.
Book Synopsis Practical Handbook of Genetic Algorithms by : Lance D. Chambers
Download or read book Practical Handbook of Genetic Algorithms written by Lance D. Chambers and published by CRC Press. This book was released on 2019-09-17 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Handbook of Genetic Algorithms, Volume 3: Complex Coding Systems contains computer-code examples for the development of genetic algorithm systems - compiling them from an array of practitioners in the field. Each contribution of this singular resource includes: unique code segments documentation descripti
Book Synopsis The Practical Handbook of Genetic Algorithms by : Lance D. Chambers
Download or read book The Practical Handbook of Genetic Algorithms written by Lance D. Chambers and published by CRC Press. This book was released on 2000-12-07 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid developments in the field of genetic algorithms along with the popularity of the first edition precipitated this completely revised, thoroughly updated second edition of The Practical Handbook of Genetic Algorithms. Like its predecessor, this edition helps practitioners stay up to date on recent developments in the field and provides material
Book Synopsis Genetic Algorithms in Applications by : Rustem Popa
Download or read book Genetic Algorithms in Applications written by Rustem Popa and published by BoD – Books on Demand. This book was released on 2012-03-21 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - algorithms that search for solutions to optimization problems by "evolving" better and better solutions. Genetic Algorithms have been applied in science, engineering, business and social sciences. This book consists of 16 chapters organized into five sections. The first section deals with some applications in automatic control, the second section contains several applications in scheduling of resources, and the third section introduces some applications in electrical and electronics engineering. The next section illustrates some examples of character recognition and multi-criteria classification, and the last one deals with trading systems. These evolutionary techniques may be useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Genetic Algorithms in their applications for the first time. These applications may be useful to many other people who are getting familiar with the subject of Genetic Algorithms.
Book Synopsis Genetic Algorithms and Engineering Optimization by : Mitsuo Gen
Download or read book Genetic Algorithms and Engineering Optimization written by Mitsuo Gen and published by John Wiley & Sons. This book was released on 1999-12-28 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Im Mittelpunkt dieses Buches steht eines der wichtigsten Optimierungsverfahren der industriellen Ingenieurtechnik: Mit Hilfe genetischer Algorithmen lassen sich Qualität, Design und Zuverlässigkeit von Produkten entscheidend verbessern. Das Verfahren beruht auf der Wahrscheinlichkeitstheorie und lehnt sich an die Prinzipien der biologischen Vererbung an: Die Eigenschaften des Produkts werden, unter Beachtung der äußeren Randbedingungen, schrittweise optimiert. Ein hochaktueller Band international anerkannter Autoren. (03/00)
Book Synopsis Evolutionary Learning Algorithms for Neural Adaptive Control by : Dimitris C. Dracopoulos
Download or read book Evolutionary Learning Algorithms for Neural Adaptive Control written by Dimitris C. Dracopoulos and published by Springer. This book was released on 2013-12-21 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.
Book Synopsis Genetic Algorithms + Data Structures = Evolution Programs by : Zbigniew Michalewicz
Download or read book Genetic Algorithms + Data Structures = Evolution Programs written by Zbigniew Michalewicz and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques has been growing in the last decade, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. It is aimed at researchers, practitioners, and graduate students in computer science and artificial intelligence, operations research, and engineering. This second edition includes several new sections and many references to recent developments. A simple example of genetic code and an index are also added. Writing an evolution program for a given problem should be an enjoyable experience - this book may serve as a guide to this task.
Book Synopsis An Introduction to Genetic Algorithms by : Melanie Mitchell
Download or read book An Introduction to Genetic Algorithms written by Melanie Mitchell and published by MIT Press. This book was released on 1998-03-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
Book Synopsis Nature-Inspired Methods for Metaheuristics Optimization by : Fouad Bennis
Download or read book Nature-Inspired Methods for Metaheuristics Optimization written by Fouad Bennis and published by Springer Nature. This book was released on 2020-01-17 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.
