Author : Manuel Grana
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 17,18 MB
Release : 2006-03-30
Category : Computers
ISBN : 1846281172
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 : Dan Simon
Publisher : John Wiley & Sons
Page : 776 pages
File Size : 40,55 MB
Release : 2013-06-13
Category : Mathematics
ISBN : 1118659503
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 : Xinjie Yu
Publisher : Springer
Page : 0 pages
File Size : 26,49 MB
Release : 2012-09-05
Category : Computers
ISBN : 9781447125693
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 : Kay Chen Tan
Publisher : Springer Science & Business Media
Page : 314 pages
File Size : 32,34 MB
Release : 2005-05-04
Category : Computers
ISBN : 9781852338367
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 : Seyedali Mirjalili
Publisher : Springer
Page : 164 pages
File Size : 19,54 MB
Release : 2018-06-26
Category : Technology & Engineering
ISBN : 3319930257
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 : Fernando Orejas
Publisher : Springer Science & Business Media
Page : 1098 pages
File Size : 48,13 MB
Release : 2001-06-27
Category : Computers
ISBN : 3540422870
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 : Carlos Coello Coello
Publisher : Springer Science & Business Media
Page : 810 pages
File Size : 25,40 MB
Release : 2007-08-26
Category : Computers
ISBN : 0387367977
This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
Author : Laura F. Landweber
Publisher : Springer Science & Business Media
Page : 348 pages
File Size : 49,41 MB
Release : 2012-12-06
Category : Computers
ISBN : 364255606X
The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. Genetic algorithms meanwhile grew out of the realization that a computer program could use the biologically-inspired processes of mutation, recombination, and selection to solve hard optimization problems. Genetic and evolutionary programming provide further approaches to a wide variety of computational problems. A synthesis of these experiences reveals fundamental insights into both the computational nature of biological evolution and processes of importance to computer science. Topics include biological models of nucleic acid information processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the origin and evolution of the genetic code; and the interface with genetic algorithms and genetic and evolutionary programming.
Author : Pedro Domingos
Publisher : Basic Books
Page : 354 pages
File Size : 44,53 MB
Release : 2015-09-22
Category : Computers
ISBN : 0465061923
Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
Author : Kenneth A. De Jong
Publisher : MIT Press
Page : 267 pages
File Size : 28,51 MB
Release : 2006-02-03
Category : Computers
ISBN : 0262303337
A clear and comprehensive introduction to the field of evolutionary computation that takes an integrated approach. Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artifacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, Evolutionary Computation is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.
