An associative architecture for genetic algorithm-based machine learning

Authors:
Kirk Twardoswski
Affiliations:
-
Venue:
Computer
Year:
1994

Citing 6
Cited 5

Classifier systems and genetic algorithms

Artificial Intelligence
Vector models for data-parallel computing

Vector models for data-parallel computing
WASP: A WSI associative string processor

Journal of VLSI Signal Processing Systems
A Dynamic Associative Processor for Machine Vision Applications

IEEE Micro
An Associative Processing Module for a Heterogeneous Vision Architecture

IEEE Micro
A General-Purpose CMOS Associative Processor IC and System

IEEE Micro

A Learning Classifier Systems Bibliography

Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography

IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Fusion in sensor networks with communication constraints

Proceedings of the 3rd international symposium on Information processing in sensor networks
A one instruction set architecture for genetic algorithms

Biocomputing
Exploring parallelization for medium access schemes on many-core software defined radio architecture

Proceedings of the second workshop on Software radio implementation forum

Quantified Score

Hi-index	4.10

Visualization

Abstract

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.