An Evolving Ecosystems Approach to Generating Complex Agent Behaviour
We propose an evolving ecosystem approach to evolving complex agent behaviour based on the principle of natural selection. The agents start with very limited functional design and morphology and neural controllers are concurrently evolved as functional wholes. The agents are ‘grounded’ in an increasingly complex environment by a complex model metabolism and interaction dynamics. Furthermore, we introduce a novel criterion for evaluating differential reproductive success aimed at maximising evolutionary freedom. We also present first experimental results suggesting that this approach may be conducive to widening the scope of artificial evolution for the generation of agents exhibiting non-trivial behaviours in a complex ecosystem.
Published inProcs of the 2007 IEEE Symposium on Artificial Life (CI-SLife 2007)
RelationsSchool of Computer Science
School of Engineering and Computer Science