Paper Info
Title
Evolution And Learning In Neural Networks - An Experimental Study
Abstract
This contribution offers an experimental study of the influence of learning on evolution in populations bf neural networks in which evolutionary and learning fitness surfaces are set and known in advance. Although not biologically plausible, this allows us to investigate various hypotheses regarding the interaction between evolution and learning in neural networks, such as "neighbourhood correlation" and "relearning", in easily controled conditions. Experimental results are presented comparing the evolution of neural networks, with and without learning and on similar and dissimilar tasks. The results chart the evolutionary progress of neural network populations in terms of fitness at birth and fitness after lifetime learning on the different tasks presented and with different selection pressures.
Year
DOI
Venue
1998
10.1109/ICSMC.1998.725017
1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5
Keywords
Field
DocType
genetic algorithms(26A0), neural networks(25A0), machine learning(35A0)
Evolutionary acquisition of neural topologies,Competitive learning,Evolutionary robotics,Computer science,Recurrent neural network,Types of artificial neural networks,Artificial intelligence,Deep learning,Artificial neural network,Machine learning,Learning classifier system
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Brian Carse125926.31