Genetic Algorithms In Search Optimization And Machine Learning

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Genetic Algorithms in Search, Optimization, and Machine Learning

Author : David Edward Goldberg
Publisher : Addison-Wesley Professional
ISBN 13 :
Total Pages : 436 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Genetic Algorithms in Search, Optimization, and Machine Learning by : David Edward Goldberg

Download or read book Genetic Algorithms in Search, Optimization, and Machine Learning written by David Edward Goldberg and published by Addison-Wesley Professional. This book was released on 1989 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Search, Optimization, and Machine Learning

Author : David Edward Goldberg
Publisher :
ISBN 13 :
Total Pages : 412 pages
Book Rating : 4.:/5 (919 download)

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Book Synopsis Genetic Algorithms in Search, Optimization, and Machine Learning by : David Edward Goldberg

Download or read book Genetic Algorithms in Search, Optimization, and Machine Learning written by David Edward Goldberg and published by . This book was released on 2002 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:

An Introduction to Genetic Algorithms

Author : Melanie Mitchell
Publisher : MIT Press
ISBN 13 : 9780262631853
Total Pages : 226 pages
Book Rating : 4.6/5 (318 download)

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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.

Hands-On Genetic Algorithms with Python

Author : Eyal Wirsansky
Publisher : Packt Publishing Ltd
ISBN 13 : 1838559183
Total Pages : 334 pages
Book Rating : 4.8/5 (385 download)

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Book Synopsis Hands-On Genetic Algorithms with Python by : Eyal Wirsansky

Download or read book Hands-On Genetic Algorithms with Python written by Eyal Wirsansky and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook 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 learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho 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.

Genetic Algorithms and Machine Learning for Programmers

Author : Frances Buontempo
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680506587
Total Pages : 307 pages
Book Rating : 4.6/5 (85 download)

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Book Synopsis Genetic Algorithms and Machine Learning for Programmers by : Frances Buontempo

Download or read book Genetic Algorithms and Machine Learning for Programmers written by Frances Buontempo and published by Pragmatic Bookshelf. This book was released on 2019-01-23 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you. Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems. In this book, you will: Use heuristics and design fitness functions. Build genetic algorithms. Make nature-inspired swarms with ants, bees and particles. Create Monte Carlo simulations. Investigate cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection methods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters. Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon. What You Need: Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.

Machine Learning Control – Taming Nonlinear Dynamics and Turbulence

Author : Thomas Duriez
Publisher : Springer
ISBN 13 : 3319406248
Total Pages : 211 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Machine Learning Control – Taming Nonlinear Dynamics and Turbulence by : Thomas Duriez

Download or read book Machine Learning Control – Taming Nonlinear Dynamics and Turbulence written by Thomas Duriez and published by Springer. This book was released on 2016-11-02 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.

Learning Genetic Algorithms with Python

Author : Ivan Gridin
Publisher : BPB Publications
ISBN 13 : 8194837758
Total Pages : 330 pages
Book Rating : 4.1/5 (948 download)

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Book Synopsis Learning Genetic Algorithms with Python by : Ivan Gridin

Download or read book Learning Genetic Algorithms with Python written by Ivan Gridin and published by BPB Publications. This book was released on 2021-02-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Refuel your AI Models and ML applications with High-Quality Optimization and Search Solutions DESCRIPTION Genetic algorithms are one of the most straightforward and powerful techniques used in machine learning. This book ÔLearning Genetic Algorithms with PythonÕ guides the reader right from the basics of genetic algorithms to its real practical implementation in production environments.Ê Each of the chapters gives the reader an intuitive understanding of each concept. You will learn how to build a genetic algorithm from scratch and implement it in real-life problems. Covered with practical illustrated examples, you will learn to design and choose the best model architecture for the particular tasks. Cutting edge examples like radar and football manager problem statements, you will learn to solve high-dimensional big data challenges with ways of optimizing genetic algorithms. KEY FEATURESÊÊ _ Complete coverage on practical implementation of genetic algorithms. _ Intuitive explanations and visualizations supply theoretical concepts. _ Added examples and use-cases on the performance of genetic algorithms. _ Use of Python libraries and a niche coverage on the performance optimization of genetic algorithms. WHAT YOU WILL LEARNÊ _ Understand the mechanism of genetic algorithms using popular python libraries. _ Learn the principles and architecture of genetic algorithms. _ Apply and Solve planning, scheduling and analytics problems in Enterprise applications. _Ê Expert learning on prime concepts like Selection, Mutation and Crossover. WHO THIS BOOK IS FORÊÊ The book is for Data Science team, Analytics team, AI Engineers, ML Professionals who want to integrate genetic algorithms to refuel their ML and AI applications. No special expertise about machine learning is required although a basic knowledge of Python is expected. TABLE OF CONTENTS 1. Introduction 2. Genetic Algorithm Flow 3. Selection 4. Crossover 5. Mutation 6. Effectiveness 7. Parameter Tuning 8. Black-box Function 9. Combinatorial Optimization: Binary Gene Encoding 10. Combinatorial Optimization: Ordered Gene Encoding 11. Other Common Problems 12. Adaptive Genetic Algorithm 13. Improving Performance

Genetic Algorithms + Data Structures = Evolution Programs

Author : Zbigniew Michalewicz
Publisher : Springer Science & Business Media
ISBN 13 : 3662033151
Total Pages : 392 pages
Book Rating : 4.6/5 (62 download)

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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 392 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 is still growing, 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. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.

Essentials of Metaheuristics (Second Edition)

Author : Sean Luke
Publisher :
ISBN 13 : 9781300549628
Total Pages : 242 pages
Book Rating : 4.5/5 (496 download)

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Book Synopsis Essentials of Metaheuristics (Second Edition) by : Sean Luke

Download or read book Essentials of Metaheuristics (Second Edition) written by Sean Luke and published by . This book was released on 2012-12-20 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interested in the Genetic Algorithm? Simulated Annealing? Ant Colony Optimization? Essentials of Metaheuristics covers these and other metaheuristics algorithms, and is intended for undergraduate students, programmers, and non-experts. The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 71 figures and 135 algorithms great and small. Algorithms include: Gradient Ascent techniques, Hill-Climbing variants, Simulated Annealing, Tabu Search variants, Iterated Local Search, Evolution Strategies, the Genetic Algorithm, the Steady-State Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Genetic Programming variants, One- and Two-Population Competitive Coevolution, N-Population Cooperative Coevolution, Implicit Fitness Sharing, Deterministic Crowding, NSGA-II, SPEA2, GRASP, Ant Colony Optimization variants, Guided Local Search, LEM, PBIL, UMDA, cGA, BOA, SAMUEL, ZCS, XCS, and XCSF.

Genetic Algorithms

Author : David E. Goldberg
Publisher : Pearson Education India
ISBN 13 : 9788177588293
Total Pages : 432 pages
Book Rating : 4.5/5 (882 download)

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Book Synopsis Genetic Algorithms by : David E. Goldberg

Download or read book Genetic Algorithms written by David E. Goldberg and published by Pearson Education India. This book was released on 2013-02 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, suitable for both course work and self-study, brings together for the first time, in an informal, tutorial fashion, the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields: programmers, scientists, engineers, mathematicians, statisticians and management scientists will all find interesting possibilities here. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. Chapter concludes with exercises and computer assignments. No prior knowledge of Gas or genetics is assumed.

Artificial Intelligence Methods in the Environmental Sciences

Author : Sue Ellen Haupt
Publisher : Springer Science & Business Media
ISBN 13 : 1402091192
Total Pages : 418 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Artificial Intelligence Methods in the Environmental Sciences by : Sue Ellen Haupt

Download or read book Artificial Intelligence Methods in the Environmental Sciences written by Sue Ellen Haupt and published by Springer Science & Business Media. This book was released on 2008-11-28 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Advanced Data Mining and Applications

Author : Changjie Tang
Publisher : Springer
ISBN 13 : 3540881921
Total Pages : 759 pages
Book Rating : 4.5/5 (48 download)

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Book Synopsis Advanced Data Mining and Applications by : Changjie Tang

Download or read book Advanced Data Mining and Applications written by Changjie Tang and published by Springer. This book was released on 2008-09-30 with total page 759 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fourth International Conference on Advanced Data Mining and Applications (ADMA 2008) will be held in Chengdu, China, followed by the last three successful ADMA conferences (2005 in Wu Han, 2006 in Xi'an, and 2007 Harbin). Our major goal of ADMA is to bring together the experts on data mining in the world, and to provide a leading international forum for the dissemination of original research results in data mining, including applications, algorithms, software and systems, and different disciplines with potential applications of data mining. This goal has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. ADMA is ranked higher than, or very similar to, other data mining conferences (such as PAKDD, PKDD, and SDM) in early 2008 by an independent source: cs-conference-ranking. org. This year we had the pleasure and honor to host illustrious keynote speakers. Our distinguished keynote speakers are Prof. Qiang Yang and Prof. Jiming Liu. Prof. Yang is a tenured Professor and postgraduate studies coordinator at Computer Science and Engineering Department of Hong Kong University of Science and Technology. He is also a member of AAAI, ACM, a senior member of the IEEE, and he is also an as- ciate editor for the IEEE TKDE and IEEE Intelligent Systems, KAIS and WI Journals.

Introduction to Evolutionary Computing

Author : Agoston E. Eiben
Publisher : Springer Science & Business Media
ISBN 13 : 3662050943
Total Pages : 307 pages
Book Rating : 4.6/5 (62 download)

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Book Synopsis Introduction to Evolutionary Computing by : Agoston E. Eiben

Download or read book Introduction to Evolutionary Computing written by Agoston E. Eiben and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

Advances in Metaheuristics for Hard Optimization

Author : Patrick Siarry
Publisher : Springer Science & Business Media
ISBN 13 : 3540729607
Total Pages : 481 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Advances in Metaheuristics for Hard Optimization by : Patrick Siarry

Download or read book Advances in Metaheuristics for Hard Optimization written by Patrick Siarry and published by Springer Science & Business Media. This book was released on 2007-12-06 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many advances have recently been made in metaheuristic methods, from theory to applications. The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and metaheuristics.

Machine Learning

Author : Hojjat Adeli
Publisher :
ISBN 13 :
Total Pages : 232 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Machine Learning by : Hojjat Adeli

Download or read book Machine Learning written by Hojjat Adeli and published by . This book was released on 1995 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the only book to apply neural nets, genetic algorithms, and fuzzy set theory to the fast growing field of machine learning. Placing particular emphasis on neural networks, it explores how to integrate them with other technologies to improve their performance. Examples are included for each system discussed.

The Design of Innovation

Author : David E. Goldberg
Publisher : Springer Science & Business Media
ISBN 13 : 1475736436
Total Pages : 259 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis The Design of Innovation by : David E. Goldberg

Download or read book The Design of Innovation written by David E. Goldberg and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: 7 69 6 A DESIGN APPROACH TO PROBLEM DIFFICULTY 71 1 Design and Problem Difficulty 71 2 Three Misconceptions 72 3 Hard Problems Exist 76 4 The 3-Way Decomposition and Its Core 77 The Core of Intra-BB Difficulty: Deception 5 77 6 The Core of Inter-BB Difficulty: Scaling 83 7 The Core of Extra-BB Difficulty: Noise 88 Crosstalk: All Roads Lead to the Core 8 89 9 From Multimodality to Hierarchy 93 10 Summary 100 7 ENSURING BUILDING BLOCK SUPPLY 101 1 Past Work 101 2 Facetwise Supply Model I: One BB 102 Facetwise Supply Model II: Partition Success 103 3 4 Population Size for BB Supply 104 Summary 5 106 8 ENSURING BUILDING BLOCK GROWTH 109 1 The Schema Theorem: BB Growth Bound 109 2 Schema Growth Somewhat More Generally 111 3 Designing for BB Market Share Growth 112 4 Selection Press ure for Early Success 114 5 Designing for Late in the Day 116 The Schema Theorem Works 6 118 A Demonstration of Selection Stall 7 119 Summary 122 8 9 MAKING TIME FOR BUILDING BLOCKS 125 1 Analysis of Selection Alone: Takeover Time 126 2 Drift: When Selection Chooses for No Reason 129 3 Convergence Times with Multiple BBs 132 4 A Time-Scales Derivation of Critical Locus 142 5 A Little Model of Noise-Induced Run Elongation 143 6 From Alleles to Building Blocks 147 7 Summary 148 10 DECIDING WELL 151 1 Why is Decision Making a Problem? 151

Introduction to Genetic Algorithms

Author : S.N. Sivanandam
Publisher : Springer Science & Business Media
ISBN 13 : 3540731903
Total Pages : 442 pages
Book Rating : 4.5/5 (47 download)

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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 442 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.