The Coevolution of Antibodies for Concept Learning
Mitchell A. Potter
Kenneth A. De Jong
We present a novel approach to concept learning in which a coevolutionary genetic algorithm is applied to the construction of an immune system whose antibodies can discriminate between examples and counter-examples of a given concept. This approach is more general than traditional symbolic approaches to concept learning and can be applied in situations where preclassified training examples are not necessarily available. An experimental study is described in which a coevolutionary immune system adapts itself to one of the standard machine learning data sets. The resulting immune system concept description and a description produced by a traditional symbolic concept learner are compared and contrasted.