MIT Press Journal Article on Neural Networks

Abstract
An important question in neuroevolution is how to gain an advantage from evolving
neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology
method on a challenging benchmark reinforcement learning task. We claim that the
increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation
studies that demonstrate that each component is necessary to the system as a whole
and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both
optimize and complexify solutions simultaneously, offering the possibility of evolving
increasingly complex solutions over generations, and strengthening the analogy with
biological evolution.