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Author: Oliver Kramer Publisher: Springer ISBN: 331952156X Category : Technology & Engineering Languages : en Pages : 94
Book Description
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.
Author: Oliver Kramer Publisher: Springer ISBN: 331952156X Category : Technology & Engineering Languages : en Pages : 94
Book Description
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.
Author: Melanie Mitchell Publisher: MIT Press ISBN: 9780262631853 Category : Computers Languages : en Pages : 226
Book Description
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.
Author: Frances Buontempo Publisher: ISBN: 9781680506204 Category : Artificial intelligence Languages : en Pages : 0
Book Description
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to machine learning. Discover machine learning algorithms using a handful of self-contained recipes. Create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, and cellular automata. Find minima and maxima, using hill climbing and simulated annealing. Try selection mathods, including tournament and roulette wheels. Learn about heuristics, fitness functions, metrics, and clusters.
Author: Tapan P. Bagchi Publisher: Springer Science & Business Media ISBN: 9780792385615 Category : Business & Economics Languages : en Pages : 384
Book Description
Multiobjective Scheduling by Genetic Algorithms describes methods for developing multiobjective solutions to common production scheduling equations modeling in the literature as flowshops, job shops and open shops. The methodology is metaheuristic, one inspired by how nature has evolved a multitude of coexisting species of living beings on earth. Multiobjective flowshops, job shops and open shops are each highly relevant models in manufacturing, classroom scheduling or automotive assembly, yet for want of sound methods they have remained almost untouched to date. This text shows how methods such as Elitist Nondominated Sorting Genetic Algorithm (ENGA) can find a bevy of Pareto optimal solutions for them. Also it accents the value of hybridizing Gas with both solution-generating and solution-improvement methods. It envisions fundamental research into such methods, greatly strengthening the growing reach of metaheuristic methods. This book is therefore intended for students of industrial engineering, operations research, operations management and computer science, as well as practitioners. It may also assist in the development of efficient shop management software tools for schedulers and production planners who face multiple planning and operating objectives as a matter of course.
Author: Randy L. Haupt Publisher: John Wiley & Sons ISBN: 0470106271 Category : Science Languages : en Pages : 317
Book Description
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
Author: S.N. Sivanandam Publisher: Springer Science & Business Media ISBN: 3540731903 Category : Technology & Engineering Languages : en Pages : 453
Book Description
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.
Author: Kim-Fung Man Publisher: Springer Science & Business Media ISBN: 144710577X Category : Mathematics Languages : en Pages : 346
Book Description
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.
Author: David Edward Goldberg Publisher: Addison-Wesley Professional ISBN: Category : Computers Languages : en Pages : 436
Book Description
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.
Author: Gabriel Luque Publisher: Springer Science & Business Media ISBN: 3642220835 Category : Computers Languages : en Pages : 173
Book Description
This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific fields (parallelism and GAs) is always difficult, and the book seeks at gracefully introducing from basic concepts to advanced topics. The presentation is structured in three parts. The first one is targeted to the algorithms themselves, discussing their components, the physical parallelism, and best practices in using and evaluating them. A second part deals with the theory for pGAs, with an eye on theory-to-practice issues. A final third part offers a very wide study of pGAs as practical problem solvers, addressing domains such as natural language processing, circuits design, scheduling, and genomics. This volume will be helpful both for researchers and practitioners. The first part shows pGAs to either beginners and mature researchers looking for a unified view of the two fields: GAs and parallelism. The second part partially solves (and also opens) new investigation lines in theory of pGAs. The third part can be accessed independently for readers interested in applications. The result is an excellent source of information on the state of the art and future developments in parallel GAs.
Author: Oliver Kramer
Author: Oliver Kramer
Author: Melanie Mitchell
Author: Frances Buontempo
Author: Tapan P. Bagchi
Author: Randy L. Haupt
Author: S.N. Sivanandam
Author: Kim-Fung Man
Author: David Edward Goldberg
Author: Michael D. Vose
Author: Gabriel Luque