Iba / Hasegawa / Paul Applied Genetic Programming and Machine Learning

1. Auflage 2010
ISBN: 978-1-4398-0370-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 349 Seiten

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.

Autoren/Hrsg.

Iba, Hitoshi

Hasegawa, Yoshihiko

Paul, Topon Kumar

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Introduction

Genetic Programming
Introduction to Genetic Programming
LGPC System

Numerical Approach to Genetic Programming
Introduction
Background
Numerical Problems with STROGANOFF
Classification Problems Solved by STROGANOFF
Temporal Problems Solved by STROGANOFF
Financial Applications by STROGANOFF
Inductive Genetic Programming
Discussion
Summary

Classification by Ensemble of Genetic Programming Rules
Background
Various Classifiers
Various Feature Selection Methods
Classification by Genetic Programming
Various Ensemble Techniques
Applying MVGPC to Real-world Problems
Extension of MVGPC: Various Performance Improvement Techniques
Summary

Probabilistic Program Evolution
Background
General EDA
Prototype Tree-based Methods
PCFG-based Methods
Other Related Methods
Summary
Appendix: GUI Systems and Source Codes
References
Index