Applied genetic programming and machine learning /
Iba, Hitoshi.
Applied genetic programming and machine learning / Hitoshi Iba, Topon Kumar Paul, Yoshihiko Hasegawa. - Boca Raton : CRC Press, c2010. - xxvi, 327 p. : ill. ; 25 cm. - The CRC Press international series on computational intelligence .
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
9781439803691 (hc : alk. paper)
2009019804
Machine learning.
Genetic programming (Computer science)
Hasegawa, Yoshihiko.
006.31
Applied genetic programming and machine learning / Hitoshi Iba, Topon Kumar Paul, Yoshihiko Hasegawa. - Boca Raton : CRC Press, c2010. - xxvi, 327 p. : ill. ; 25 cm. - The CRC Press international series on computational intelligence .
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
9781439803691 (hc : alk. paper)
2009019804
Machine learning.
Genetic programming (Computer science)
Hasegawa, Yoshihiko.
006.31