Abstract:
Classification is a machine learning technique
used to predict group membership for data instances.
To simplify the problem of classification neural
networks are being introduced. In this paper, the
adaptation of network weights using Island Model
based Differential Evolution (IMDE) was proposed
as a mechanism to improve the performance of
Artificial Neural Network (ANN). Differential
Evolution (DE) has been used to determine optimal
value for ANN parameters such as learning rate and
momentum rate and also for weight optimization.
Island model used multiple subpopulations and
exchanges the individual to boost the overall
performance of the algorithm. In this paper, fully
connected topology is being used. This system
proposes an island model based differential evolution
algorithm to enhance the learning speed of neural
network training. The results have revealed that
IMDENN has given quite promising results in terms
of convergence rate smaller errors compared to other
algorithms.
