Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems

Abstract This paper describes one aspect of a machine-learning system called HELPR that blends the best aspects of different evolutionary techniques to bootstrap-up a complete recognition system from primitive input data. HELPR uses a multi-faceted representation consisting of a growing sequence of non-linear mathematical expressions. Individual features are represented as tree structures and manipulated using the techniques of genetic programming. Sets of features are represented as list structures that are manipulated using genetic algorithms and evolutionary programming. Complete recognition systems are formed in this version of HELPR by attaching the evolved features to multiple perceptron discriminators. Experiments on datasets from the University of California at Irvine (UCI) machine-learning repository show that HELPR’s performance meets or exceeds accuracies previously published.

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