Exploiting Environmental Differentiation to Promote Evolvability in Artificial Evolution

In this work we investigate the possibility to exploit environmental differentiation to promote evolvability in artificial evolution. More specifically we propose a new algorithm and demonstrate how agents evolved for the ability to solve the double-pole balancing problem in differentiated environmental conditions among the population outperform agents evolved in homogeneous environmental conditions. The algorithm operates by evolving the agents on multiple environmental niches with randomly varying environmental characteristics and by enabling agents displaying superior performance in other niches to colonize them. Agents evolved through the proposed algorithm outperform agents evolved in homogeneous environments, either on stable or temporally varying environments.