DMCA
Evolving Neural Networks through Augmenting Topologies (0)
| Venue: | Evolutionary Computation |
| Citations: | 535 - 112 self |
BibTeX
@ARTICLE{Stanley_evolvingneural,
author = {Kenneth O. Stanley and Risto Miikkulainen},
title = {Evolving Neural Networks through Augmenting Topologies},
journal = {Evolutionary Computation},
year = {},
volume = {10},
pages = {2002}
}
Share
 |
 |
 |
 |
||
OpenURL
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.
Keyphrases
augmenting topology neural network structural innovation important question minimal structure challenging benchmark reinforcement biological evolution important contribution complex solution fixed-topology method increased efficiency different topology neural network topology principled method ablation study
