Creating High-Level Components with a Generative
Representation for Body-Brain Evolution
Artificial Life, 8 (3):
223--246 (Summer 2002)DOI: doi:10.1162/106454602320991837
Abstract
One of the main limitations of scalability in
body-brain evolution systems is the representation
chosen for encoding creatures. This paper defines a
class of representations called generative
representations, which are identified by their ability
to reuse elements of the genotype in the translation to
the phenotype. This paper presents an example of a
generative representation for the concurrent evolution
of the morphology and neural controller of simulated
robots, and also introduces GENRE, an evolutionary
system for evolving designs using this representation.
Applying GENRE to the task of evolving robots for
locomotion and comparing it against a non-generative
(direct) representation shows that the generative
representation system rapidly produces robots with
significantly greater fitness. Analyzing these results
shows that the generative representation system
achieves better performance by capturing useful bias
from the design space and by allowing viable large
scale mutations in the phenotype. Generative
representations thereby enable the encapsulation,
coordination, and reuse of assemblies of parts.
