logo
Product categories

EbookNice.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link.  https://ebooknice.com/page/post?id=faq


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookNice Team

(Ebook) Applied Genetic Programming and Machine Learning (Crc Press International Series on Computational Intelligence) by Hitoshi Iba, Yoshihiko Hasegawa, Topon Kumar Paul ISBN 9781439803691, 1439803692

  • SKU: EBN-1844884
Zoomable Image
$ 32 $ 40 (-20%)

Status:

Available

5.0

40 reviews
Instant download (eBook) Applied Genetic Programming and Machine Learning (Crc Press International Series on Computational Intelligence) after payment.
Authors:Hitoshi Iba, Yoshihiko Hasegawa, Topon Kumar Paul
Pages:338 pages.
Year:2009
Editon:1
Publisher:CRC Press
Language:english
File Size:4.3 MB
Format:pdf
ISBNS:9781439803691, 1439803692
Categories: Ebooks

Product desciption

(Ebook) Applied Genetic Programming and Machine Learning (Crc Press International Series on Computational Intelligence) by Hitoshi Iba, Yoshihiko Hasegawa, Topon Kumar Paul ISBN 9781439803691, 1439803692

What do financial data prediction, day-trading rule development, and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field, Applied Genetic Programming and Machine Learning delineates the extension of Genetic Programming (GP) for practical applications. Reflecting rapidly developing concepts and emerging paradigms, this book outlines 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. It provides a methodology for integrating GP and machine learning techniques, establishing a robust evolutionary framework for addressing tasks from areas such as chaotic time-series prediction, system identification, financial forecasting, classification, and data mining. The book provides a starting point for the research of extended GP frameworks with the integration of several machine learning schemes. Drawing on empirical studies taken from fields such as system identification, finanical engineering, and bio-informatics, it demonstrates how the proposed methodology can be useful in practical inductive problem solving.
*Free conversion of into popular formats such as PDF, DOCX, DOC, AZW, EPUB, and MOBI after payment.

Related Products