Efficient Approaches to Interleaved Sampling of Training Data for Symbolic Regression

Azad, R. Muhammad Atif and Medernach, David and Ryan, Conor (2014) Efficient Approaches to Interleaved Sampling of Training Data for Symbolic Regression. In: Proceedings of 6th World Congress on Nature and Biologically Inspired Computing (NaBIC 2014). IEEE Press, Porto, Portugal, pp. 176-183. ISBN 978-l-4799-5937-2114

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Abstract

The ability to generalize beyond the training set is paramount for any machine learning algorithm and Genetic Programming (GP) is no exception. This paper investigates a recently proposed technique to improve generalisation in GP, termed Interleaved Sampling where GP alternates between using the entire data set and only a single data point in alternate
generations. This paper proposes two alternatives to using a single data point : the use of random search instead of a single data point, and simply minimising the tree size. Both the approaches are more efficient than the original Interleaved Sampling because they simply do not evaluate the fitness in half the number of generations. The results show that in terms
of generalisation, random search and size minimisation are as effective as the original Interleaved Sampling; however, they are computationally more efficient in terms of data processing. Size minimisation is particularly interesting because it completely prevents bloat while still being competitive in terms of training results as well as generalisation. The tree sizes with size minimisation are substantwlly smaller reducing the computational expense substantially.

Item Type: Book Section
Uncontrolled Keywords: optimisation; Genetic Programming; over fitting;
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Enterprise Systems
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: Oana-Andreea Dumitrascu
Date Deposited: 12 Jun 2017 12:35
Last Modified: 11 Oct 2017 07:48
URI: http://www.open-access.bcu.ac.uk/id/eprint/4602

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