  The ability of a classifier to take on new information and classes by
evolving the classifier without it having to be fully retrained is known as
incremental learning. Incremental learning has been successfully applied to
many classification problems, where the data is changing and is not all
available at once. In this paper there is a comparison between Learn++, which
is one of the most recent incremental learning algorithms, and the new proposed
method of Incremental Learning Using Genetic Algorithm (ILUGA). Learn++ has
shown good incremental learning capabilities on benchmark datasets on which the
new ILUGA method has been tested. ILUGA has also shown good incremental
learning ability using only a few classifiers and does not suffer from
catastrophic forgetting. The results obtained for ILUGA on the Optical
Character Recognition (OCR) and Wine datasets are good, with an overall
accuracy of 93% and 94% respectively showing a 4% improvement over Learn++.MT
for the difficult multi-class OCR dataset.
