Progressive Minimal Criteria Novelty Search
We propose progressive minimal criteria novelty search (PMCNS), which is an extension of minimal criteria novelty search. In PMCNS, we combine the respective benefits of novelty search and fitness-based evolution by letting novelty search freely explore new regions of behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our approach in the evolution of neurocontrollers for a swarm of robots in a coordination task where robots must share a single charging station. The robots can only survive by periodically recharging their batteries. We compare the performance of PMCNS with (i) minimal criteria novelty search, (ii) pure novelty search, (iii) pure fitness-based evolution, and (iv) with evolutionary search based on a linear blend of novelty and fitness. Our results show that PMCNS outperforms all four approaches. Finally, we analyse how different parameter setting in PMCNS influence the exploration of the behaviour space.
Keywordsnovelty search evolutionary swarm robotics deception
Unable to display preview. Download preview PDF.
- 4.Lehman, J., Stanley, K.O.: Revising the Evolutionary Computation Abstraction – Minimal Criteria Novelty Search. In: 2010 Genetic and Evolutionary Computation Conference (GECCO 2010), pp. 103–110. ACM, New York (2010)Google Scholar
- 6.Lehman, J., Stanley, K.O.: Evolving a Diversity of Virtual Creatures through Novelty Search and Local Competition. In: 2011 Genetic and Evolutionary Computation Conference (GECCO 2011), pp. 211–218. ACM, New York (2011)Google Scholar
- 7.Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.C., Floreano, D., Martinoli, A.: The e-puck, a Robot Designed for Education in Engineering. In: 9th Conference on Autonomous Robot Systems and Competitions (Robotica 2009), pp. 59–65. IPCB, Castelo Branco (2009)Google Scholar
- 10.Risi, S., Vanderbleek, S.D., Hughes, C.E., Stanley, K.O.: How Novelty Search Escapes the Deceptive Trap of Learning to Learn. In: 2009 Genetic and Evolutionary Computation Conference (GECCO 2009), pp. 153–160. ACM, New York (2009)Google Scholar