Abstract:
In data mining, multilayer feed-forward
networks are one of the most used neural networks
in various domains because of their universal
approximation capability. Back-propagation (BP)
uses two phases, feed-forward and backpropagate,
for learning the weights in the network
and training multilayer feed-forward network.The
main disadvantage of the back-propagation
algorithm is its convergence rate is slow and it is
always being trapped in local minima. Differential
evolution (DE) algorithm is a population based
algorithm like genetic algorithms using similar
operators; crossover, mutation and selection. In
this paper, the performance of DE algorithm is
compared to BP algorithm. From the
experimental results, it was observed that the
accuracy of DE is better than BP.This paper
evaluates the performance of two algorithms by
using hold out validation method and then shows
the comparison results with bar chart. IRIS, breast
cancer and wine data set from UCI - University of
California at Irvine (Machine Learning
Repositories and Domain Theories) is applied for
classification. This paper is implemented by using
netbean with java programming language.
