Brian D. Davison
Genetic Algorithms (GAs) purport to mimic the behavior of natural selection. Many GAs, however, try to optimize their populations by means of a static fitness function --one that is derived from performance on a fixed set of examples. We propose an architecture for an online genetic algorithm (OLGA) for classification. An OLGA differs from standard genetic algorithms in that it does not repeatedly evaluate individuals against a fixed set of training examples. Instead, it is presented with a series of training examples, one at a time, and does not retain the entire set for training.