Experimental supplements to the computational complexity analysis of genetic programming for problems modelling isolated program semantics

Abstract

In this paper, we carry out experimental investigations that complement recent theoretical investigations on the runtime of simple genetic programming algorithms [3, 7]. Crucial measures in these theoretical analyses are the maximum tree size that is attained during the run of the algorithms as well as the population size when dealing with multi-objective models. We study those measures in detail by experimental investigations and analyze the runtime of the different algorithms in an experimental way. © 2012 Springer-Verlag.