Ordered incremental training with genetic algorithms

Incremental training has been used for GA-based classifiers in a dynamic environment where training samples or new attributes/classes become available over time. In this paper, ordered incremental genetic algorithms (OIGAs) are proposed to address the incremental training of input attributes for classifiers. Rather than learning input attributes in batch as with normal GAs, OIGAs learn input attributes one after another. The resulting classification rule sets are also evolved incrementally to accommodate the new attributes. Furthermore, attributes are arranged in different orders by evaluating their individual discriminating ability. By experimenting with different attribute orders, different approaches of OIGAs are evaluated using four benchmark classification data sets. Their performance is also compared with normal GAs. The simulation results show that OIGAs can achieve generally better performance than normal GAs. The order of attributes does have an effect on the final classifier performance where OIGA training with a descending order of attributes performs the best.