Evolving a statistics class using object oriented evolutionary programming

Authors:
Alexandros Agapitos;Simon M. Lucas
Affiliations:
Department of Computer Science, University of Essex, Colchester, UK;Department of Computer Science, University of Essex, Colchester, UK
Venue:
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Year:
2007

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Abstract

Object Oriented Evolutionary Programming is used to evolve programs that calculate some statistical measures on a set of numbers.We compared this technique with a more standard functional representation. We also studied the effects of scalar and Pareto-based multi-objective fitness functions to the induction of multi-task programs. We found that the induction of a program residing in an OO representation space is more efficient, yielding less fitness evaluations, and that scalar fitness performed better than Pareto-based fitness in this problem domain.