Applied genetic programming and machine learning / Hitoshi Iba, Topon Kumar Paul, Yoshihiko Hasegawa.

By:

Iba, Hitoshi

Contributor(s):

Paul, Topon Kumar

Material type: Text

TextSeries: The CRC Press international series on computational intelligencePublication details: Boca Raton : CRC Press, c2010.Description: xxvi, 327 p. : ill. ; 25 cmISBN:

9781439803691 (hc : alk. paper)

Subject(s):

DDC classification:

006.31 22

Contents:

Genetic Programming -- Numerical Approach to Genetic Programming -- Classification by Ensemble of Genetic Programming Rules -- Probabilistic Program Evolution.

Abstract: Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. Written by a leading authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source codes and GUIs are available for download from the author's website

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Books	Main library General Stacks	006.31 / IB.A 2010 (Browse shelf(Opens below))	1	Available		007539

Includes bibliographical references and index.

Genetic Programming -- Numerical Approach to Genetic Programming -- Classification by Ensemble of Genetic Programming Rules -- Probabilistic Program Evolution.

Reflecting rapidly developing concepts and newly emerging paradigms in intelligent machines, this text is the first to integrate genetic programming and machine learning techniques to solve diverse real-world tasks.These tasks include financial data prediction, day-trading rule development; and bio-marker selection. Written by a leading authority, this text will teach readers how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the search process through the design of objective fitness functions, and examine the search performance of the evolutionary system. All source codes and GUIs are available for download from the author's website

There are no comments on this title.

to post a comment.