Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding

Abstract

Modular robots oer an important benet in evolutionaryrobotics, which is to quickly evaluate evolved morphologies and controlsystems in reality. However, articial evolution of simulated modularrobotics is a dicult and time consuming task requiring signicant computationalpower. While articial evolution in virtual creatures has madeuse of powerful generative encodings, here we investigate how a generativeencoding and direct encoding compare for the evolution of locomotionin modular robots when the number of robotic modules changes.Simulating less modules would decrease the size of the genome of a directencoding while the size of the genome of the implemented generativeencoding stays the same. We found that the generative encoding is signicantly more ecient in creating robot phenotypes in the initial stagesof evolution when simulating a maximum of 5, 10, and 20 modules. Thisnot only conrms that generative encodings lead to decent performancemore quickly, but also that when simulating just a few modules a generativeencoding is more powerful than a direct encoding for creatingrobotic structures. Over longer evolutionary time, the dierence betweenthe encodings no longer becomes statistically signicant. This leads us tospeculate that a combined approach { starting with a generative encodingand later implementing a direct encoding { can lead to more ecientevolved designs