Competitive Coevolution through Evolutionary Complexification
  Two major goals in machine learning are the discovery and improvement of
solutions to complex problems. In this paper, we argue that complexification,
i.e. the incremental elaboration of solutions through adding new structure,
achieves both these goals. We demonstrate the power of complexification through
the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves
increasingly complex neural network architectures. NEAT is applied to an
open-ended coevolutionary robot duel domain where robot controllers compete
head to head. Because the robot duel domain supports a wide range of
strategies, and because coevolution benefits from an escalating arms race, it
serves as a suitable testbed for studying complexification. When compared to
the evolution of networks with fixed structure, complexifying evolution
discovers significantly more sophisticated strategies. The results suggest that
in order to discover and improve complex solutions, evolution, and search in
general, should be allowed to complexify as well as optimize.
