An incremental genetic algorithm and neural network for classification and sensitivity analysis of their parameters

This study proposes classification by using algorithms that are inspired by computation in biological systems and to compare of them. These are genetic algorithm and neural network. New incremental genetic algorithm and new incremental neural network algorithm are developed for classification for efficiently handling new transactions. To achieve incremental classification, a specific model that includes all information about a train operation, rules for each class for genetic algorithm and weight values for neural network is created after each training operation. Later, these models are used for testing, correctness test, comparing models and incremental classification. With that new incremental method, training time gets smaller for new dataset. Experimental results proof that assumption. This paper introduces that new method and importance of that method. This study also includes the sensitivity analysis of the incremental and traditional genetic algorithm parameters and neural network parameters. In this analysis, many specific models were created using the same training dataset but with different parameter values, and then the performances of the models were compared. To achieve these operations two tools are developed for both genetic algorithm and neural network and all of these investigations are done by using these tools.