Immune-Based optimization of predicting neural networks

Authors:
Aleksander Byrski;Marek Kisiel-Dorohinicki
Affiliations:
Institute of Computer Science, AGH University of Science and Technology, Cracow, Poland;Institute of Computer Science, AGH University of Science and Technology, Cracow, Poland
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Year:
2005

Citing 4
Cited 1

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Neural, Novel and Hybrid Algorithms for Time Series Prediction

Neural, Novel and Hybrid Algorithms for Time Series Prediction
Machine Learning

Machine Learning
Agent-Oriented Model of Simulated Evolution

SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics

Agent-Based Evolutionary and Immunological Optimization

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Artificial immune systems turned out to be an interesting technique introduced into the area of soft computing. In the paper the idea of an immunological selection mechanism in the agent-based optimization of a neural network architecture is presented. General considerations are illustrated by the particular system dedicated to time-series prediction. Selected experimental results conclude the work.