- author = "Hitoshi Iba and Yoshihiko Hasegawa and Topon Kumar Paul",
- title = "Applied Genetic Programming and Machine Learning",
- publisher = "CRC",
- year = "2009",
- series = "CRC Complex and Enterprise Systems Engineering",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "1-4398-0369-2",
-
URL = "
http://www.crcpress.com/product/isbn/9781439803691",
- 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.",
-
notes = "Book reviews in \cite{Veeramachaneni:2011:GPEM}
\cite{journals/mima/Osman12}.
Introduction. Evolutionary computation. Genetic Programming. Hybrid Genetic Programming and GMDH System. Principles of STROGANOFF. Classification by Ensemble of Genetic Programming Rules. Probabilistic Program Evolution with Estimation of Distribution. Other Related Methods. Discussion. Conclusion. Appendix A: STROGANOFF system overviews. Appendix B: MVGPC system overviews.",
- size = "349 pages",
