Aging in artificial learning systems

Authors:
Sarunas Raudys
Affiliations:
Department of Informatics, MIF, Vilnius University, Vilnius, Lithuania
Venue:
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Year:
2007

Citing 5
Cited 0

The immune system, adaptation, and machine learning

Physica D
Statistical and neural classifiers: an integrated approach to design

Statistical and neural classifiers: an integrated approach to design
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
Group interests of agents functioning in changing environments

CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

While looking at aging of individuals, we take for granted that one of aging reasons is related with individual's ability to learn rapidly, to adapt to sudden environmental changes and survive. We explain maturation and aging of standard non-linear single layer perceptron by increasing of components of the weight vector and dramatic decline of a gradient. We analyze also artificial immune system trained by the mutation based genetic learning algorithm. In both, the connectionist and genetic learning, we obtain saturation and an inverted letter "U" shape dependence between success in learning and the "age".