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Empirical modeling using genetic programming: a survey of issues and approaches

Empirical modeling using genetic programming: a survey of issues and approaches Empirical modeling, which is a process of developing a mathematical model of a system from experimental data, has attracted many researchers due to its wide applicability. Finding both the structure and appropriate numeric coefficients of the model is a real challenge. Genetic programming (GP) has been applied by many practitioners to solve this problem. However, there are a number of issues which require careful attention while applying GP to empirical modeling problems. We begin with highlighting the importance of these issues including: computational efforts in evolving a model, premature convergence, generalization ability of an evolved model, building hierarchical models, and constant creation techniques. We survey and classify different approaches used by GP researchers to deal with the mentioned issues. We present different performance measures which are useful to report the results of analysis of GP runs. We hope this work would help the reader by facilitating to understand key concepts and practical issues of GP and steering in selection of an appropriate approach to solve a particular issue effectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Natural Computing Springer Journals

Empirical modeling using genetic programming: a survey of issues and approaches

Natural Computing , Volume 14 (2) – Feb 26, 2014

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References (31)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Computer Science; Theory of Computation; Evolutionary Biology; Processor Architectures; Artificial Intelligence (incl. Robotics); Statistical Physics, Dynamical Systems and Complexity
ISSN
1567-7818
eISSN
1572-9796
DOI
10.1007/s11047-014-9416-y
Publisher site
See Article on Publisher Site

Abstract

Empirical modeling, which is a process of developing a mathematical model of a system from experimental data, has attracted many researchers due to its wide applicability. Finding both the structure and appropriate numeric coefficients of the model is a real challenge. Genetic programming (GP) has been applied by many practitioners to solve this problem. However, there are a number of issues which require careful attention while applying GP to empirical modeling problems. We begin with highlighting the importance of these issues including: computational efforts in evolving a model, premature convergence, generalization ability of an evolved model, building hierarchical models, and constant creation techniques. We survey and classify different approaches used by GP researchers to deal with the mentioned issues. We present different performance measures which are useful to report the results of analysis of GP runs. We hope this work would help the reader by facilitating to understand key concepts and practical issues of GP and steering in selection of an appropriate approach to solve a particular issue effectively.

Journal

Natural ComputingSpringer Journals

Published: Feb 26, 2014

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