Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition

We describe a multiple classifier system which incorporates an automatic self-configuration scheme based on genetic algorithms. Our main interest in this paper is focused on exploring the statistical properties of the resulting multi-expert configurations. To this end we initially test the proposed system on a series of tasks of increasing difficulty drawn from the domain of character recognition. We then proceed to investigate the performance of our system not only in comparison to that of its constituent classifiers, but also in comparison to an independent set of individually optimised classifiers. Our results illustrate that significant gains can be obtained by integrating a genetic algorithm based optimisation process into multi-classifier schemes both in the performance enhancement and in the reduction of its volatility, especially as the task domain becomes more complex.