Issue No. 11 - November (1994 vol. 27)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/2.330041
<p>Machine-based learning will eventually be applied to solve real-world problems. In this work, an associative architecture teams up with hybrid AI algorithms to solve a letter prediction problem with promising results. This article describes an investigation and simulation of a massively parallel learning classifier system (LCS) that was developed from a specialized associative architecture joined with hybrid AI algorithms. The LCS algorithms were specifically invented to computationally match a massively parallel computer architecture, which was a special-purpose design to support the inferencing and learning components of the LCS. The LCS's computationally intensive functions include rule matching, parent selection, replacement selection and, to a lesser degree, data structure manipulation.</p>
K. Twardoswski, "An associative architecture for genetic algorithm-based machine learning," in Computer, vol. 27, no. , pp. 27-38, 1994.