On September 6th, 2017, the International Society for Artificial Life (ISAL) awarded Ken Stanley and Risto Miikkulainen the Award for Outstanding Paper of the Decade (2002-2012) for their paper “Evolving Neural Networks Through Augmenting Topologies”, Evolutionary Computation 10:99-127 (2002).
Risto Miikkulainen is currently the VP of Research at Sentient Technologies and is a professor of neural networks and evolutionary computation at the University of Texas in Austin. Originally from Finland, Risto holds a Master of Science in Applied Mathematics from Aalto University in Helsinki (formerly Helsinki University of Technology) and a PhD in Computer Science from the University of California, Los Angeles.
This paper was the first to describe the NEAT method for evolving neural networks that has become widely used in the field.
The abstract reads:
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) that 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.
Curious to read more? The full paper is accessible here.