Performance models for evolutionary program induction algorithms based on problem difficulty indicators

Authors:
Mario Graff;Riccardo Poli
Affiliations:
Division de Estudios de Posgrado, Facultad de Ingenieria Electrica, Universidad Michoacana de San Nicolas de Hidalgo, Mexico;School of Computer Science and Electronic Engineering, University of Essex, UK
Venue:
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Year:
2011

Citing 14
Cited 1

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Abstract

Most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. In this paper, two models of evolutionary program-induction algorithms (EPAs) are proposed which overcome this limitation. We test our approach with two important classes of problems -- symbolic regression and Boolean function induction -- and a variety of EPAs including: different versions of genetic programming, gene expression programing, stochastic iterated hill climbing in program space and one version of cartesian genetic programming. We compare the proposed models against a practical model of EPAs we previously developed and find that in most cases the new models are simpler and produce better predictions. A great deal can also be learnt about an EPA via a simple inspection of our new models. E.g., it is possible to infer which characteristics make a problem difficult or easy for the EPA.