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An Introduction to Genetic Algorithms

An Introduction to Genetic Algorithms Author Melanie Mitchell
ISBN-10 0262631857
Release 1998
Pages 209
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Genetic algorithms are used in science and engineering for problem solving and as computational models. This brief introduction enables readers to implement and experiment with genetic algorithms on their own. The descriptions of applications and modeling projects stretch beyond the boundaries of computer science to include systems theory, game theory, biology, ecology, and population genetics. 20 illustrations.

Introduction to Genetic Algorithms

Introduction to Genetic Algorithms Author S.N. Sivanandam
ISBN-10 9783540731900
Release 2007-10-24
Pages 442
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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.

An Introduction to Genetic Algorithms for Scientists and Engineers

An Introduction to Genetic Algorithms for Scientists and Engineers Author David A. Coley
ISBN-10 9810236026
Release 1999
Pages 227
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This invaluable book has been designed to be useful to most practising scientists and engineers, whatever their field and however rusty their mathematics and programming might be. The approach taken is largely practical, with algorithms being presented in full and working code (in BASIC, FORTRAN, PASCAL AND C) included on a floppy disk to help the reader get up and running as quickly as possible. The text could also be used as part of an undergraduate course on search and optimisation. Student exercises are included at the end of several of the chapters, many of which are computer-based and designed to encourage exploration of the method.

Introduction to Fuzzy Logic using MATLAB

Introduction to Fuzzy Logic using MATLAB Author S.N. Sivanandam
ISBN-10 9783540357810
Release 2006-10-28
Pages 430
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This book provides a broad-ranging, but detailed overview of the basics of Fuzzy Logic. The fundamentals of Fuzzy Logic are discussed in detail, and illustrated with various solved examples. The book also deals with applications of Fuzzy Logic, to help readers more fully understand the concepts involved. Solutions to the problems are programmed using MATLAB 6.0, with simulated results. The MATLAB Fuzzy Logic toolbox is provided for easy reference.

Genetic Algorithm Essentials

Genetic Algorithm Essentials Author Oliver Kramer
ISBN-10 9783319521565
Release 2017-02-22
Pages 92
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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.

Genetic Algorithms Data Structures Evolution Programs

Genetic Algorithms   Data Structures   Evolution Programs Author Zbigniew Michalewicz
ISBN-10 9783662028308
Release 2013-06-29
Pages 252
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'What does your Master teach?' asked a visitor. 'Nothing,' said the disciple. 'Then why does he give discourses?' 'He only points the way - he teaches nothing.' Anthony de Mello, One Minute Wisdom During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The emergence of massively par allel computers made these algorithms of practical interest. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies, simulated annealing, classifier systems, and neural net works. Recently (1-3 October 1990) the University of Dortmund, Germany, hosted the First Workshop on Parallel Problem Solving from Nature [164]. This book discusses a subclass of these algorithms - those which are based on the principle of evolution (survival of the fittest). In such algorithms a popu lation of individuals (potential solutions) undergoes a sequence of unary (muta tion type) and higher order (crossover type) transformations. These individuals strive for survival: a selection scheme, biased towards fitter individuals, selects the next generation. After some number of generations, the program converges - the best individual hopefully represents the optimum solution. There are many different algorithms in this category. To underline the sim ilarities between them we use the common term "evolution programs" .

Genetic Algorithms in Java Basics

Genetic Algorithms in Java Basics Author Lee Jacobson
ISBN-10 9781484203286
Release 2015-11-28
Pages 154
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Genetic Algorithms in Java Basics is a brief introduction to solving problems using genetic algorithms, with working projects and solutions written in the Java programming language. This brief book will guide you step-by-step through various implementations of genetic algorithms and some of their common applications, with the aim to give you a practical understanding allowing you to solve your own unique, individual problems. After reading this book you will be comfortable with the language specific issues and concepts involved with genetic algorithms and you'll have everything you need to start building your own. Genetic algorithms are frequently used to solve highly complex real world problems and with this book you too can harness their problem solving capabilities. Understanding how to utilize and implement genetic algorithms is an essential tool in any respected software developers toolkit. So step into this intriguing topic and learn how you too can improve your software with genetic algorithms, and see real Java code at work which you can develop further for your own projects and research. Guides you through the theory behind genetic algorithms Explains how genetic algorithms can be used for software developers trying to solve a range of problems Provides a step-by-step guide to implementing genetic algorithms in Java

Genetic Algorithms in Electromagnetics

Genetic Algorithms in Electromagnetics Author Randy L. Haupt
ISBN-10 9780470106273
Release 2007-04-27
Pages 288
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A thorough and insightful introduction to using genetic algorithms to optimize electromagnetic systems Genetic Algorithms in Electromagnetics focuses on optimizing the objective function when a computer algorithm, analytical model, or experimental result describes the performance of an electromagnetic system. It offers expert guidance to optimizing electromagnetic systems using genetic algorithms (GA), which have proven to be tenacious in finding optimal results where traditional techniques fail. Genetic Algorithms in Electromagnetics begins with an introduction to optimization and several commonly used numerical optimization routines, and goes on to feature: Introductions to GA in both binary and continuous variable forms, complete with examples of MATLAB(r) commands Two step-by-step examples of optimizing antenna arrays as well as a comprehensive overview of applications of GA to antenna array design problems Coverage of GA as an adaptive algorithm, including adaptive and smart arrays as well as adaptive reflectors and crossed dipoles Explanations of the optimization of several different wire antennas, starting with the famous "crooked monopole" How to optimize horn, reflector, and microstrip patch antennas, which require significantly more computing power than wire antennas Coverage of GA optimization of scattering, including scattering from frequency selective surfaces and electromagnetic band gap materials Ideas on operator and parameter selection for a GA Detailed explanations of particle swarm optimization and multiple objective optimization An appendix of MATLAB code for experimentation

Practical Genetic Algorithms

Practical Genetic Algorithms Author Randy L. Haupt
ISBN-10 0471671754
Release 2004-07-30
Pages 272
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* This book deals with the fundamentals of genetic algorithms and their applications in a variety of different areas of engineering and science * Most significant update to the second edition is the MATLAB codes that accompany the text * Provides a thorough discussion of hybrid genetic algorithms * Features more examples than first edition

Genetic Algorithms with Python

Genetic Algorithms with Python Author Clinton Sheppard
ISBN-10 1732029806
Release 2018-02-11
Pages 532
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Get a hands-on introduction to machine learning with genetic algorithms using Python. Genetic algorithms are one of the tools you can use to apply machine learning to finding good, sometimes even optimal, solutions to problems that have billions of potential solutions. This book gives you experience making genetic algorithms work for you, using easy-to-follow example projects that you can fall back upon when learning to use other machine learning tools and techniques. The step-by-step tutorials build your skills from Hello World! to optimizing one genetic algorithm with another, and finally genetic programming; thus preparing you to apply genetic algorithms to problems in your own field of expertise. Python is a high-level, low ceremony and powerful language whose code can be easily understood even by entry-level programmers. If you have experience with another programming language then you should have no difficulty learning Python by induction. Souce code: https: //


Complexity Author Melanie Mitchell
ISBN-10 9780199724574
Release 2009-04-01
Pages 368
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What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of neurons produce something as extraordinarily complex as consciousness? In this remarkably clear and companionable book, leading complex systems scientist Melanie Mitchell provides an intimate tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. Richly illustrated, Complexity: A Guided Tour--winner of the 2010 Phi Beta Kappa Book Award in Science--offers a wide-ranging overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for its contribution to solving some of the most important scientific questions of our time.

Introduction to Evolutionary Algorithms

Introduction to Evolutionary Algorithms Author Xinjie Yu
ISBN-10 1849961298
Release 2010-06-10
Pages 422
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Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.

Genetic Algorithms

Genetic Algorithms Author Kim-Fung Man
ISBN-10 1852330724
Release 2001-02-19
Pages 344
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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.

Genetic Algorithms in Molecular Modeling

Genetic Algorithms in Molecular Modeling Author James Devillers
ISBN-10 9780080532387
Release 1996-06-07
Pages 327
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Genetic Algorithms in Molecular Modeling is the first book available on the use of genetic algorithms in molecular design. This volume marks the beginning of an ew series of books, Principles in Qsar and Drug Design, which will be an indispensible reference for students and professionals involved in medicinal chemistry, pharmacology, (eco)toxicology, and agrochemistry. Each comprehensive chapter is written by a distinguished researcher in the field. Through its up to the minute content, extensive bibliography, and essential information on software availability, this book leads the reader from the theoretical aspects to the practical applications. It enables the uninitiated reader to apply genetic algorithms for modeling the biological activities and properties of chemicals, and provides the trained scientist with the most up to date information on the topic. . Extremely topical and timely . Sets the foundations for the development of computer-aided tools for solving numerous problems in QSAR and drug design . Written to be accessible without prior direct experience in genetic algorithms

Classification and Learning Using Genetic Algorithms

Classification and Learning Using Genetic Algorithms Author Sanghamitra Bandyopadhyay
ISBN-10 9783540496076
Release 2007-05-17
Pages 311
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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.

Genetic Algorithms in Engineering Systems

Genetic Algorithms in Engineering Systems Author Ali M. S. Zalzala
ISBN-10 0852969023
Release 1997-01-01
Pages 263
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Arising out of the highly successful 1st IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA '95), held at the University of Sheffield UK, this book comprises commissioned papers from some of the highest quality contributions to the conference. Chosen for their experience in the field, the authorship is international and drawn from academia and industry. The chapters cover the main fields of work as well as presenting tutorial material in this important subject, which is currently receiving considerable attention from engineers.

Genetic Algorithms and Investment Strategies

Genetic Algorithms and Investment Strategies Author Richard J. Bauer
ISBN-10 0471576794
Release 1994-02-21
Pages 308
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When you combine nature′s efficiency and the computer′s speed, the financial possibilities are almost limitless. Today′s traders and investment analysts require faster, sleeker weaponry in today′s ruthless financial marketplace. Battles are now waged at computer speed, with skirmishes lasting not days or weeks, but mere hours. In his series of influential articles, Richard Bauer has shown why these professionals must add new computerized decision–making tools to their arsenal if they are to succeed. In Genetic Algorithms and Investment Strategies, he uniquely focuses on the most powerful weapon of all, revealing how the speed, power, and flexibility of GAs can help them consistently devise winning investment strategies. The only book to demonstrate how GAs can work effectively in the world of finance, it first describes the biological and historical bases of GAs as well as other computerized approaches such as neural networks and chaos theory. It goes on to compare their uses, advantages, and overall superiority of GAs. In subsequently presenting a basic optimization problem, Genetic Algorithms and Investment Strategies outlines the essential steps involved in using a GA and shows how it mimics nature′s evolutionary process by moving quickly toward a near–optimal solution. Introduced to advanced variations of essential GA procedures, readers soon learn how GAs can be used to: ∗ Solve large, complex problems and smaller sets of problems ∗ Serve the needs of traders with widely different investment philosophies ∗ Develop sound market timing trading rules in the stock and bond markets ∗ Select profitable individual stocks and bonds ∗ Devise powerful portfolio management systems Complete with information on relevant software programs, a glossary of GA terminology, and an extensive bibliography covering computerized approaches and market timing, Genetic Algorithms and Investment Strategies unveils in clear, nontechnical language a remarkably efficient strategic decision–making process that, when imaginatively used, enables traders and investment analysts to reap significant financial rewards.