This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks.
Learning classifier systems (LCS) are a framework for adaptively subdividing input spaces into an ensemble of simpler local approximations that together cover the domain.
The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies.
Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms.
Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives.
We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under approximated wind tunnel conditions after being physically instantiated by a 3D printer.
A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks.