Introduction to Evolutionary Algorithms
Book Details:
Publisher: | Springer |
Series: |
Springer
|
Author: | Xinjie Yu |
Edition: | 1 |
ISBN-10: | 184996128X |
ISBN-13: | 9781849961288 |
Pages: | 422 |
Published: | Jun 23 2010 |
Posted: | Nov 19 2014 |
Language: | English |
Book format: | PDF |
Book size: | 4.64 MB |
Book Description:
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.
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 theor...
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....
An Introduction to Distributed Algorithms takes up some of the main concepts and algorithms, ranging from basic to advanced techniques and applications, that underlie the programming of distributed-memory systems such as computer networks, networks of workstations, and multiprocessors. Written from the broad perspective of distributed-memory systems in general it includes topics such as algorithms for maximum flow, program debugging, and simulation that do not appear in more orthodox texts on distributed algorithms. Moving from fundamentals to advances and applications, ten chapters-with exercises and bibliographic notes-cover a variety of topics. These include models of distributed computation, information propagation, leader election, distributed ...
2007 - 2021 © eBooks-IT.org