Abstract
The recursive deterministic perceptron (RDP) is a generalization of the single layer perceptron neural network. This neural network can separate, in a deterministic manner, any classification problem (linearly separable or not). It relies on the principle that in any nonlinearly separable (NLS) two-class classification problem, a linearly separable (LS) subset of one or more points belonging to one of the two classes can always be found. Small network topologies can be obtained when the LS subsets are of maximum cardinality. This is referred to as the problem of maximum separability and has been proven to be NP-Complete. Evolutionary computing techniques are applied to handle this problem in a more efficient way than the standard approaches in terms of complexity. These techniques enhance the RDP training in terms of speed of conversion and level of generalization. They provide an alternative to tackle large classification problems which is otherwise not feasible with the algorithmic versions of the RDP training methods.
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