The Evolution of General Intelligence (2014)
When studying different species in the wild, field biologists can see enormous variation in their behaviors and learning abilities. For example, spotted hyenas and baboons share the same habitat and have similar levels of complexity in their social interactions, but differ widely in how specific vs. general their behaviors are. This paper analyzes two potential factors that lead to this difference: the density of connections in the brain, and the number of generations in prolonged evolution (i.e. after a solution has been found). Using neuroevolution with the NEAT algorithm, network structures with different connectivities were evaluated in recognizing digits and their mirror images. These experiments show that general intelligence, i.e. recognition of previously unseen examples, increases with increase in connectivity, up to a point. General intelligence also increases with the number of generations in prolonged evolution, even when performance no longer improves in the known examples. This outcome suggests that general intelligence depends on specific anatomical and environmental factors. The results from this paper can be used to gain insight into differences in animal behaviors, as well as a guideline for constructing complex general behaviors in artificial agents such as video game bots and physical robots.
In Proceedings of The Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14), New York, NY, 2014.

Kay E. Holekamp Collaborator holekamp [at] msu edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
Padmini Rajagopalan Postdoctoral Alumni padminir [at] utexas edu