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Evolving Cellular Automata as Pattern Classifier
Niloy Ganguly
1
, Pradipta Maji
2
, Sandip Dhar
2
, Biplab K. Sikdar
2
, and
P. Pal Chaudhuri
2
1
Computer centre, IISWBM, Calcutta, West Bengal, India 700073,
n ganguly@hotmail.com
2
Department of Computer Science & Technology, Bengal Engineering College (D U),
Howrah, West Bengal, India 711103
{
pradipta,sdhar,biplab,ppc
}
@cs.becs.ac.in
Abstract.
This paper reports a high speed, low cost pattern classifier
based on the sparse network of Cellular Automata. High quality of classi-
fication of patterns with or without noise has been demonstrated through
theoretical analysis supported with extensive experimental results.
1 Introduction
The internetworked society has been experiencing a explosion of data that is
acting as an impediment in acquiring knowledge. The meaningful interpretation
of these data is increasingly becoming di
%
cult. Consequently, researchers, prac-
titioners, entrepreneurs from diverse fields are assembling together to develop
sophisticated techniques for knowledge extraction. Study of data classification
models form the basis of such research. A classification model comprises of two
basic operations - classification and prediction. The evolving
CA
based classifier
proposed in this paper derives its strength from the following features:
-
The special class of
CA
referred to as Multiple Attractor Cellular Automata
(
MACA
) is evolved with the help of geneticalgorithm to arrive at the desired
model of
CA
based classifier.
-
In the prediction phase the classifier is capable of accommodating noise based
on distance metric.
-
The classifier employs the simple computing model of three neighborhood
Additive
CA
having very high throughput. Further, the simple, regular,
modular and local neighborhood sparse network of Cellular Automata suits
ideally for low cost
VLSI
implementation.
The Cellular Automata (
CA
) preliminaries follows in the next section.
2 Cellular Automata Preliminaries
The fundamentals of Cellular Automata we deal with is reported in the topic [1].
The classifier reported in this work has been developed around a specific class
of
CA
referred to as
Multiple Attractor CA
(
MACA
).
