A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is
Evolving neural networks for fractured domains
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08
Evolution of neural networks, or neuroevolution, bas been successful on many low-level control problems such as pole balancing, vehicle control, and collision warning. However, high-level strategy problems that require the integration of multiple sub-behaviors have remained difficult for neuroevolution to solve. This paper proposes the hypothesis that such problems are difficult because they are fractured: the correct action varies discontinuously as the agent moves from state to state. Thisdoi:10.1145/1389095.1389366 dblp:conf/gecco/KohlM08 fatcat:znya5pbmfvdihnenufjtvvcfyu