Parameter space structure of continuous-time recurrent neural networks
Neural Computation
Robot design for space missions using evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolving complete robots with CPPN-NEAT: the utility of recurrent connections
Proceedings of the 13th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Compared with fixed morphology robotic systems, self-reconfigurable modular (SRM) robots can reconfigure themselves to form a variety of morphologies, and carry on various types of motions. Recently, some co-evolutionary approaches have been proposed to co-evolve the robot morphology and associated controller simultaneously for locomotion tasks. However, these co-evolution approaches don't consider some physical limitations of SRM robots and usually request longer evolution process due to extensive searching space. To address these issues, we proposed a species-based co-evolution (S-CoE) algorithm. The S-CoE algorithm is applied on a simulated modular robot system and evaluated under two testing scenarios.