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.
… 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
Introduction to Genetic Programming
Numerical Approach to Genetic Programming
Numerical Problems with STROGANOFF
Classification Problems Solved by STROGANOFF
Temporal Problems Solved by STROGANOFF
Financial Applications by STROGANOFF
Inductive Genetic Programming
Classification by Ensemble of Genetic Programming Rules
Various Feature Selection Methods
Classification by Genetic Programming
Various Ensemble Techniques
Applying MVGPC to Real-world Problems
Extension of MVGPC: Various Performance Improvement Techniques
Probabilistic Program Evolution
Prototype Tree-based Methods
Other Related Methods
Appendix: GUI Systems and Source Codes