The characters described here are not necessarily intended to represent humans or any other creatures known to exist in nature. Rather than defining attributes of a particular creature, a genetic algorithm is used to evolve basic building blocks into more complex beings. These beings may then develop the ability to swim, walk, jump and even respond to light sources. Each creature is defined by a directed graph where each node contains information that describes a rigid part of that creature. The type of neurons used in this experiment were sensors, effectors and those internal to a particular node.
Sensors measure aspects of a particular part or the world relative to that part.
The types of sensors used can measure the degrees of freedom of a joint, determine if contact was made with another part or the with environment, and calculate light source positions. Internal neurons allow the creatures to behave arbitrarily. Effectors take data from sensors and internal neurons and then control a degree of freedom of a joint appropriately. Other types of sensors can measure things like acceleration and sounds but the author believes that the three types of sensors used were sufficient. Creatures with low fitness values will be eliminated and the remaining population is used to create new generations.