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
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.
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!