The max problem revisited: the importance of mutation in genetic programming

Authors:
Timo Kötzing;Andrew M. Sutton;Frank Neumann;Una-May O'Reilly
Affiliations:
Max-Planck-Institut für Informatik, Saarbrücken, Germany;University of Adelaide, Adelaide, Australia;University of Adelaide, Adelaide, Australia;Massachusetts Institute of Technology, Cambridge, MA, USA
Venue:
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Year:
2012

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Abstract

This paper contributes to the rigorous understanding of genetic programming algorithms by providing runtime complexity analyses of the well-studied Max problem. Several experimental studies have indicated that it is hard to solve the Max problem with crossover-based algorithms. Our analyses show that different variants of the Max problem can provably be solved using simple mutation-based genetic programming algorithms. Our results advance the body of computational complexity analyses of genetic programming, indicate the importance of mutation in genetic programming, and reveal new insights into the behavior of mutation-based genetic programming algorithms.