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An Introduction To Genetic Algorithms

Author: Melanie Mitchell
Publisher: MIT Press
ISBN: 9780262631853
Size: 15.98 MB
Format: PDF, ePub, Docs
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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

Author: S.N. Sivanandam
Publisher: Springer Science & Business Media
ISBN: 3540731903
Size: 21.17 MB
Format: PDF, ePub
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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.

Introduction To Evolutionary Algorithms

Author: Xinjie Yu
Publisher: Springer Science & Business Media
ISBN: 9781849961295
Size: 32.72 MB
Format: PDF, Kindle
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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.

An Introduction To Genetic Algorithms For Scientists And Engineers

Author: David A. Coley
Publisher: World Scientific
ISBN: 9789810236021
Size: 50.61 MB
Format: PDF
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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.

An Introduction To Genetic Algorithms

Author: Associate Professor of Computer Science and Engineering Ogi School of Science and Engineering Melanie Mitchell, Cda, Bgs
Publisher: MIT Press (MA)
ISBN: 9780262280013
Size: 64.30 MB
Format: PDF, ePub
View: 1249
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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.

Genetic Algorithms In Search Optimization And Machine Learning

Author: David Edward Goldberg
Publisher: Addison-Wesley Professional
ISBN:
Size: 45.33 MB
Format: PDF, ePub, Mobi
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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.

Genetic Algorithms In Engineering Systems

Author: Ali M. S. Zalzala
Publisher: IET
ISBN: 9780852969021
Size: 15.47 MB
Format: PDF, Mobi
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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 Engineering Optimization

Author: Mitsuo Gen
Publisher: John Wiley & Sons
ISBN: 9780471315315
Size: 43.77 MB
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A comprehensive guide to a powerful new analytical tool by two of its foremost innovators The past decade has witnessed many exciting advances in the use of genetic algorithms (GAs) to solve optimization problems in everything from product design to scheduling and client/server networking. Aided by GAs, analysts and designers now routinely evolve solutions to complex combinatorial and multiobjective optimization problems with an ease and rapidity unthinkable withconventional methods. Despite the continued growth and refinement of this powerful analytical tool, there continues to be a lack of up-to-date guides to contemporary GA optimization principles and practices. Written by two of the world's leading experts in the field, this book fills that gap in the literature. Taking an intuitive approach, Mitsuo Gen and Runwei Cheng employ numerous illustrations and real-world examples to help readers gain a thorough understanding of basic GA concepts-including encoding, adaptation, and genetic optimizations-and to show how GAs can be used to solve an array of constrained, combinatorial, multiobjective, and fuzzy optimization problems. Focusing on problems commonly encountered in industry-especially in manufacturing-Professors Gen and Cheng provide in-depth coverage of advanced GA techniques for: * Reliability design * Manufacturing cell design * Scheduling * Advanced transportation problems * Network design and routing Genetic Algorithms and Engineering Optimization is an indispensable working resource for industrial engineers and designers, as well as systems analysts, operations researchers, and management scientists working in manufacturing and related industries. It also makes an excellent primary or supplementary text for advanced courses in industrial engineering, management science, operations research, computer science, and artificial intelligence.

Genetic Algorithms In Electromagnetics

Author: Randy L. Haupt
Publisher: John Wiley & Sons
ISBN: 0470106271
Size: 13.86 MB
Format: PDF
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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