BlueSANE: integrating functional blueprints with neuroevolution.
Neuroevolution algorithms are an important tool for optimizing neural network design in the fields of control and machine learning. We seek to improve SANE, a classic machine learning algorithm, by optimizing the size of the hidden layer in the neural networks that it generates. We use a technique c...
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| Online Access: | http://hdl.handle.net/2047/d20002569 |
| Summary: | Neuroevolution algorithms are an important tool for optimizing neural network design in the fields of control and machine learning. We seek to improve SANE, a classic machine learning algorithm, by optimizing the size of the hidden layer in the neural networks that it generates. We use a technique called functional blueprints that guide the self-organization of systems by specifying their desired behavior, in this case avoidance of over/underfitting. We performed experiments
with a simulated double cart-pole balancing benchmark problem which indicate that BlueSANE improves performance by slight to moderate amounts compared to the original SANE algorithm. |
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