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Applied Genetic Programming and Machine Learning (Paperback)
Loot Price: R1,907
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Applied Genetic Programming and Machine Learning (Paperback)
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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.
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