Optimisation of multiple classifier systems using genetic algorithms

We introduce a novel multiple classifier system with the ability of automatic self-configuration. The system employs a genetic algorithm to optimise the configuration of the participating individual classifiers arranged in a parallel structure. Our primary interest was to study the behaviour of such an integrated system, first in the case of increasingly complex tasks and secondly when additional information is made available in the form of increasingly larger training data sets. The fact that these cases often arise in real world applications underline their special importance in developing classifier systems that can address realistic problem domains. As an example we tested the proposed system on a character recognition task using one printed, and one handwritten, character data set. Our findings strongly suggest that significant benefit can be gained from the integration of the genetic algorithm-based optimisation process into the system in both situations