A New Wave: A Dynamic Approach to Genetic Programming

Medernach, David and Fitzgerald, Jeannie and Azad, R. Muhammad Atif and Ryan, Conor (2016) A New Wave: A Dynamic Approach to Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM, New York, pp. 757-764. ISBN 978-1-4503-4206-3

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Abstract

Wave is a novel form of semantic genetic programming which operates by optimising the residual errors of a succession of short genetic programming runs, and then producing a cumulative solution. These short genetic programming runs are called periods, and they have heterogeneous parameters. In this paper we leverage the potential of Wave's heterogeneity to simulate a dynamic evolutionary environment by incorporating self adaptive parameters together with an innovative approach to population renewal. We conduct an empirical study comparing this new approach with multiple linear regression~(MLR) as well as several evolutionary computation~(EC) methods including the well known geometric semantic genetic programming~(GSGP) together with several other optimised Wave techniques. The results of our investigation show that the dynamic Wave algorithm delivers consistently equal or better performance than Standard GP (both with or without linear scaling), achieves testing fitness equal or better than multiple linear regression, and performs significantly better than GSGP on five of the six problems studied.

Item Type:	Book Section
Identification Number:	10.1145/2908812.2908857
Dates:	Date Event 1 July 2016 Published Online
Uncontrolled Keywords:	Natural Selection, Semantic GP, Genetic Programming, Sym- bolic Regression, Self-adaptation, ensembles, residuals
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Ian Mcdonald
Date Deposited:	16 Mar 2017 12:56
Last Modified:	22 Mar 2023 12:01
URI:	https://www.open-access.bcu.ac.uk/id/eprint/4070

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