Sparse Distributed Memory
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
The Coevolution of Antibodies for Concept Learning
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Exploiting the Analogy between the Immune System and Sparse Distributed Memories
Genetic Programming and Evolvable Machines
Immune-inspired incremental feature selection technology to data streams
Applied Soft Computing
Management and forecast of dynamic customer needs: An artificial immune and neural system approach
Advanced Engineering Informatics
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In this paper we present a prototype of a new model for performing clustering in large, non-static databases. Although many machine learning algorithms for data clustering have been proposed, none appear to specifically address the task of clustering moving data. The model we describe combines features of two existing computational models -- that of Artificial Immune Systems (AIS) and Sparse Distributed Memories (SDM). The model is evolved using a coevolutionary genetic algorithm that runs continuously in order to dynamically track clusters in the data. Although the system is very much in its infancy, the experiments conducted so far show that the system is capable of tracking moving clusters in artificial data sets, and also incorporates some memory of past clusters. The results suggest many possible directions for future research.