PPSN XII - LNCS 7491-7492 CD-ROM

Experimental Supplements to the Computational Complexity Analysis of Genetic Programming for Problems Modelling Isolated Program Semantics

Tommaso Urli¹, Markus Wagner², and Frank Neumann²

¹DIEGM, Università degli Studi di Udine, 33100, Udine, Italy

²School of Computer Science, University of Adelaide, Adelaide, SA 5005, Australia

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.

Keywords: genetic programming, problem complexity, multiple-objective optimization, experimental evaluation

LNCS 7491, p. 102 ff.

Full article in PDF | BibTeX