Information Processing With Evolutionary Algorithms
Download Information Processing With Evolutionary Algorithms PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Information Processing With Evolutionary Algorithms book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Introduction to Evolutionary Algorithms
Author: Xinjie Yu
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06-10
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.
Information Processing with Evolutionary Algorithms
Author: Manuel Grana
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-03-30
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
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Author: Alex A. Freitas
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-11-11
This book addresses the integration of two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increas ingly popular in the last few years, and their integration is currently an area of active research. In essence, data mining consists of extracting valid, comprehensible, and in teresting knowledge from data. Data mining is actually an interdisciplinary field, since there are many kinds of methods that can be used to extract knowledge from data. Arguably, data mining mainly uses methods from machine learning (a branch of artificial intelligence) and statistics (including statistical pattern recog nition). Our discussion of data mining and evolutionary algorithms is primarily based on machine learning concepts and principles. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which the user can potentially use to make intelligent decisions. In a nutshell, 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 (rules or another form of knowl edge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.