Frank Veenstra, Emma Hart, Edgar Buchanan, Wei Li, Matteo De Carlo, A.E. Eiben

Comparing encodings for performance and phenotypic exploration in evolving modular robots

In Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (GECCO '19), 2019

DOI 10.1145/3319619.3322054


To investigate how encodings influence evolving the morphology and control of modular robots, we compared three encodings: a direct encoding and two generative encodings—a compositional pattern producing network (CPPN) and a Lindenmayer System (L-System). The evolutionary progression and final performance of the direct encoding and the L-System was significantly better than the CPPN. The generative encodings converge quicker than the direct encoding in terms of morphological and controller diversity.