Evolving a Statistics Class Using Object Oriented Evolutionary Programming

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.