The algorithmic beauty of plants
The algorithmic beauty of plants
“Genotypes” for neural networks
The handbook of brain theory and neural networks
A Taxonomy for artificial embryogeny
Artificial Life
Evolving 3d morphology and behavior by competition
Artificial Life
IEEE Transactions on Neural Networks
An artificial visual cortex drives behavioral evolution in co-evolved predator and prey robots
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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We have developed a novel method to "grow" neural networks according to an inherited set of production rules (the genotype), inspired by Lindenmayer systems. In the first phase (neurogenesis), the neurons proliferate in three-dimensional space by cell division, and differentiate in function, according to the production rules. In the second phase (synaptogenesis), axons emerge from the neurons and seek out connection targets. Part of each production rule is an augmented Reverse Polish Notation expression; this permits regulation of the applicable rules, as well as introduction of spatial and temporal context to the developmental process. We connect each network to a (fixed) robotic body with a set of input sensors and muscle actuators. The robot is placed in a physically simulated environment and controlled by its network for a certain time, receiving a fitness score according to its behavior (the phenotype). Mutations are introduced into offspring by making changes to their sets of production rules. This paper introduces the "L-brain" developmental method, and describes our first experiments with it, which produced controllers for robotic "spiders" with the ability to gallop, and to follow a compass heading.