Scalable Evolutionary Design of CA Pattern Classifier

The paper reports a scalable evolutionary design for pattern recognition using Multiple Attractor Cellular Automata (MACA). MACA helps to impart non-linearity in the classifier using Hamming distance based attractors. Isomorphism in MACA was exploited to make the method scalable to large classification problems involving non-linear boundaries. Extensive experimentation was performed on datasets with different topologies to establish the efficacy of the proposed method as compared to existing popular approaches like support vector machines. The classifier was applied to satellite image analysis problem. Experiments on different types of data sets were performed to discover the classifier’s feature selection capabilities.