Applied Genetic Programming and Machine Learning

by Hitoshi Iba, Yoshihiko Hasegawa, Topon Kumar Paul

Overview

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

Product Details

ISBN-13:	9781040212578
Publisher:	CRC Press
Publication date:	08/26/2009
Sold by:	Barnes & Noble
Format:	eBook
Pages:	349
File size:	14 MB
Note:	This product may take a few minutes to download.

About the Author

Iba, Hitoshi; Hasegawa, Yoshihiko; Paul, Topon Kumar

Table of Contents

Introduction. Genetic Programming. Numerical Approach to Genetic Programming. Classification by Ensemble of Genetic Programming Rules. Probabilistic Program Evolution. Appendix: GUI Systems and Source Codes. References. Index.

What People are Saying About This

From the Publisher

Applied Genetic Programming and Machine Learning provides readers with tools and insights to apply genetic programming (GP) to classical machine learning problems. … The most important aspect of this book is the accompanying code/software. Examples in the book are derived from these tools. This enables the reader to actively learn the material by practicing on the examples. … A graduate student interested in machine learning would find this book very useful. The accompanying software can be downloaded and the student can practice and understand a variety of machine learning problems. …It also serves well machine learning researchers who are interested in how genetic programming can be used for their problems. Hopefully, this will lead to application of GP to new machine learning problems such as reinforcement learning and active learning. ... a good practitioners guide for system designers employing machine learning to solve industrial problems.
— K. Veeramachaneni, Massachusetts Institute of Technology, in Genetic Programming and Evolvable Machines (2011), 12:179-180

Editorial Reviews

… an enjoyable read and offers new insights into a computational paradigm that helps to bridge the gap between applications of GP and ML, as well as substantial source code and GUI systems supporting this topic. … The best feature of this book is its simple presentation style, which includes both mathematics and visual graphics for the uninitiated. For these reasons, it is likely that the book will become an essential source of reference for students, practitioners and young researchers alike.
— Minds & Machines, 2012

... provides readers with tools and insights to apply genetic programming (GP) to classical machine learning problems. … The most important aspect of this book is the accompanying code/software. Examples in the book are derived from these tools. This enables the reader to actively learn the material by practicing on the examples. … A graduate student interested in machine learning would find this book very useful. The accompanying software can be downloaded and the student can practice and understand a variety of machine learning problems. …It also serves well machine learning researchers who are interested in how genetic programming can be used for their problems. Hopefully, this will lead to application of GP to new machine learning problems such as reinforcement learning and active learning. ... a good practitioners guide for system designers employing machine learning to solve industrial problems.
— K. Veeramachaneni, Massachusetts Institute of Technology, in Genetic Programming and Evolvable Machines (2011), 12:179-180

From the Publisher