Genetic Algorithms in Search, Optimization, and Machine Learning

Author: David Edward Goldberg,David Edward, Goldberg,Goldberg, David Edward Goldberg,Visiting Assistant Professor of History David E Goldberg

Publisher: Addison-Wesley Professional

ISBN: N.A

Category: Computers

Page: 412

View: 1636

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A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.
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Hands-On Genetic Algorithms with Python

Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

Author: Eyal Wirsansky

Publisher: Packt Publishing Ltd

ISBN: 1838559183

Category: Computers

Page: 346

View: 8715

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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.
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Genetic Algorithms

The Design of Innovation

Author: David Goldberg,Kumara Sastry

Publisher: Springer-Verlag New York Incorporated

ISBN: 9780387353746

Category: Computers

Page: 350

View: 939

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Genetic Algorithms: The Design of Innovation illustrates how to design and implement scalable genetic algorithms that solve hard problems quickly, reliably, and accurately. This revised edition of the landmark The Design of Innovation includes recent results and new groundbreaking material. The core chapters have been updated and some chapters have been thoroughly rewritten. The chapter on scalable GA design introduces other key techniques, including the Dependency Structure Matrix GA (DSMGA), which sheds light on probabilistic model builders such as the Bayesian Optimization Algorithm. A major new chapter demonstrates practical scalability of GAs on a problem with over a billion variables, and shows how these results can be used to obtain routine solutions to important problems. Genetic Algorithms is an essential reference for the innovation researcher a?? from the social and behavioral sciences, the natural sciences, the humanities, or the arts a?? or for the specialist in GAs and evolutionary computation.
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Genetic Algorithms and Fuzzy Multiobjective Optimization

Author: Masatoshi Sakawa

Publisher: Springer Science & Business Media

ISBN: 9780792374527

Category: Business & Economics

Page: 288

View: 8287

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Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness. The intended readers of this book are senior undergraduate students, graduate students, researchers, and practitioners in the fields of operations research, computer science, industrial engineering, management science, systems engineering, and other engineering disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real world research applications are used throughout the book to illustrate the presentation. These applications are drawn from complex problems. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.
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Handbook of Computational Intelligence in Manufacturing and Production Management

Author: Laha, Dipak,Mandal, Purnendu

Publisher: IGI Global

ISBN: 1599045842

Category: Computers

Page: 516

View: 5395

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During the last two decades, computer and information technologies have forced great changes in the ways businesses manage operations in meeting the desired quality of products and services, customer demands, competition, and other challenges. The Handbook of Computational Intelligence in Manufacturing and Production Management focuses on new developments in computational intelligence in areas such as forecasting, scheduling, production planning, inventory control, and aggregate planning, among others. This comprehensive collection of research provides cutting-edge knowledge on information technology developments for both researchers and professionals in fields such as operations and production management, Web engineering, artificial intelligence, and information resources management.
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Advances in Nuclear Science and Technology

Author: Jeffery Lewins,Martin Becker

Publisher: Springer Science & Business Media

ISBN: 0306478129

Category: Technology & Engineering

Page: 281

View: 3106

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The present review volume not only covers a wide range of topics pertinent to nuclear science and technology, but has attracted a distinguished international authorship, for which the editors are grateful. The opening review by Drs. Janet Tawn and Richard Wakeford addresses the difficult matter of questioning sci- tific hypotheses in a court of law. The United Kingdom experienced a substantial nuclear accident in the 1950s in the form of the Windscale Pile fire. This in itself had both good and bad consequences; the setting up of a licensing authority to ensure nuclear safety was one, the understandable public sentiment concerning nuclear power (despite the fire occurring in a weapons pile) the other. Windscale today is subsumed in the reprocessing plant at Sellafield operated by British Nuclear Fuels plc and it was inevitable perhaps that when an excess cluster of childhood leukaemia was observed in the nearby village of Seascale that public concern should be promoted by the media, leading to the hearing of a claim of compensation brought on behalf of two of the families of BNFLs workers who had suffered that loss. The review article demonstrates the complexity of und- standing such a claim against the statistical fluctuations inherent and shows how the courts were persuaded of the need to propose a biological mechanism if responsibility were to be held. The Company were undoubtedly relieved by the finding.
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Genetic Algorithms for Machine Learning

Author: John J. Grefenstette

Publisher: Springer Science & Business Media

ISBN: 9780792394075

Category: Computers

Page: 165

View: 6354

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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.
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Optimization in Practice with MATLAB

Author: Achille Messac

Publisher: Cambridge University Press

ISBN: 1107109183

Category: Mathematics

Page: 494

View: 4911

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This textbook is designed for students and industry practitioners for a first course in optimization integrating MATLAB® software.
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The Universal Generating Function in Reliability Analysis and Optimization

Author: Gregory Levitin

Publisher: Springer Science & Business Media

ISBN: 1846282454

Category: Technology & Engineering

Page: 442

View: 3988

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Many real systems are composed of multi-state components with different performance levels and several failure modes. These affect the whole system's performance. Most books on reliability theory cover binary models that allow a system only to function perfectly or fail completely. "The Universal Generating Function in Reliability Analysis and Optimization" is the first book that gives a comprehensive description of the universal generating function technique and its applications in binary and multi-state system reliability analysis. Features: - an introduction to basic tools of multi-state system reliability and optimization; - applications of the universal generating function in widely used multi-state systems; - examples of the adaptation of the universal generating function to different systems in mechanical, industrial and software engineering. This monograph will be of value to anyone interested in system reliability, performance analysis and optimization in industrial, electrical and nuclear engineering.
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