Department of Information Technology
Information processing research lab
NNGENETICS - Evolutionary Algorithms and Neural Networks

Goals of the project

Goal of this project is to analyze if genetic algorithms, especially differential evolution, can be used to train feed-forward neural networks. Disadvatanges and advantages of evolutionary methods are compared to traditional network training methods, mostly based on gradient information.


Project is finished. All the goals were met and results will be published in a scientific journal.


Contact authors for reprints.

  1. Ilonen, J., Kamarainen, J.-K., Lampinen, J., Differential Evolution Training Algorithm for Feed-Forward Neural Networks, Neural Processing Letters 7, 1 (2003), 93-105.


Matlab(tm) source code (for scientific and non-profitable use only)
Differential evolution training algorithm for Neural-Network Toolbox (tm) traindiffevol.m
Script for demonstration of the TRAINDIFFEVOL demo01.m

To download data used in our experiments, explore data repositories from links section.

Links to other resources

Differential evolution bibliographies by professor Jouni Lampinen
Local links by professor Jouni Lampinen
Differential Evolution page by Rainer Storn
Data repository: University of California
Dataset generator
Benchmark group on pattern recognition


[ML97] Masters, Timothy and Land, Walker (1997). A new training algorithm for the general regression neural network. 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation., Volume: 3, pp. 1990-1994.

Contact information

Jouni Lampinen E-mail WWW Professor, responsible author, Lappeenranta University of Technology
Joni Kamarainen E-mail WWW Researcher, Lappeenranta University of Technology
Jarmo Ilonen E-mail WWW Research assistant, Lappeenranta University of Technology

Last changes: $Date: 2003/04/26 10:27:19 $ by $Author: jkamarai $