Information Processing With Evolutionary Algorithms

Author : Manuel Grana
Publisher : Springer Science & Business Media
ISBN 13 : 1846281172
Total Pages : 340 pages
Book Rating : 4.74/5 ( download)

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Download or read book Information Processing with Evolutionary Algorithms written by Manuel Grana and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a broad sample of current information processing applications Includes examples of successful applications that will encourage practitioners to apply the techniques described in the book to real-life problems

Author : Xinjie Yu
Publisher : Springer Science & Business Media
ISBN 13 : 1849961298
Total Pages : 427 pages
Book Rating : 4.95/5 ( download)

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Download or read book Introduction to Evolutionary Algorithms written by Xinjie Yu and published by Springer Science & Business Media. This book was released on 2010-06-10 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Dan Simon
Publisher : John Wiley & Sons
ISBN 13 : 1118659503
Total Pages : 776 pages
Book Rating : 4.02/5 ( download)

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Download or read book Evolutionary Optimization Algorithms written by Dan Simon and published by John Wiley & Sons. This book was released on 2013-06-13 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Author : Kay Chen Tan
Publisher : Springer Science & Business Media
ISBN 13 : 9781852338367
Total Pages : 314 pages
Book Rating : 4.69/5 ( download)

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Download or read book Multiobjective Evolutionary Algorithms and Applications written by Kay Chen Tan and published by Springer Science & Business Media. This book was released on 2005-05-04 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary multiobjective optimization is currently gaining a lot of attention, particularly for researchers in the evolutionary computation communities. Covers the authors’ recent research in the area of multiobjective evolutionary algorithms as well as its practical applications.

Author : Witold Pedrycz
Publisher : Springer Science & Business Media
ISBN 13 : 1461561353
Total Pages : 325 pages
Book Rating : 4.54/5 ( download)

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Download or read book Fuzzy Evolutionary Computation written by Witold Pedrycz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: As of today, Evolutionary Computing and Fuzzy Set Computing are two mature, wen -developed, and higbly advanced technologies of information processing. Bach of them has its own clearly defined research agenda, specific goals to be achieved, and a wen setUed algorithmic environment. Concisely speaking, Evolutionary Computing (EC) is aimed at a coherent population -oriented methodology of structural and parametric optimization of a diversity of systems. In addition to this broad spectrum of such optimization applications, this paradigm otTers an important ability to cope with realistic goals and design objectives reflected in the form of relevant fitness functions. The GA search (which is often regarded as a dominant domain among other techniques of EC such as evolutionary strategies, genetic programming or evolutionary programming) delivers a great deal of efficiency helping navigate through large search spaces. The main thrust of fuzzy sets is in representing and managing nonnumeric (linguistic) information. The key notion (whose conceptual as weH as algorithmic importance has started to increase in the recent years) is that of information granularity. It somewhat concurs with the principle of incompatibility coined by L. A. Zadeh. Fuzzy sets form a vehic1e helpful in expressing a granular character of information to be captured. Once quantified via fuzzy sets or fuzzy relations, the domain knowledge could be used efficiently very often reducing a heavy computation burden when analyzing and optimizing complex systems.

Author : Ajith Abraham
Publisher : Springer Science & Business Media
ISBN 13 : 1846281377
Total Pages : 313 pages
Book Rating : 4.72/5 ( download)

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Download or read book Evolutionary Multiobjective Optimization written by Ajith Abraham and published by Springer Science & Business Media. This book was released on 2005-09-05 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.

Author : Fernando Orejas
Publisher : Springer Science & Business Media
ISBN 13 : 3540422870
Total Pages : 1098 pages
Book Rating : 4.77/5 ( download)

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Download or read book Automata, Languages and Programming written by Fernando Orejas and published by Springer Science & Business Media. This book was released on 2001-06-27 with total page 1098 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 28th International Colloquium on Automata, Languages and Programming, ICALP 2001, held in Crete, Greece in July 2001. The 80 revised papers presented together with two keynote contributions and four invited papers were carefully reviewed and selected from a total of 208 submissions. The papers are organized in topical sections on algebraic and circuit complexity, algorithm analysis, approximation and optimization, complexity, concurrency, efficient data structures, graph algorithms, language theory, codes and automata, model checking and protocol analysis, networks and routing, reasoning and verification, scheduling, secure computation, specification and deduction, and structural complexity.

Author : Seyedali Mirjalili
Publisher : Springer
ISBN 13 : 3319930257
Total Pages : 156 pages
Book Rating : 4.51/5 ( download)

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Download or read book Evolutionary Algorithms and Neural Networks written by Seyedali Mirjalili and published by Springer. This book was released on 2018-06-26 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.

Author : F.J. Lobo
Publisher : Springer
ISBN 13 : 3540694323
Total Pages : 318 pages
Book Rating : 4.28/5 ( download)

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Download or read book Parameter Setting in Evolutionary Algorithms written by F.J. Lobo and published by Springer. This book was released on 2007-04-03 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.

Author : Alex A. Freitas
Publisher : Springer Science & Business Media
ISBN 13 : 3662049236
Total Pages : 272 pages
Book Rating : 4.35/5 ( download)

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Download or read book Data Mining and Knowledge Discovery with Evolutionary Algorithms written by Alex A. Freitas and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topics