Title :
A study on hill climbing algorithms for neural network training
Author :
Chalup, Stephan ; Maire, Frederic
Author_Institution :
Machine Learning Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia
Abstract :
This study empirically investigates variations of hill climbing algorithms for training artificial neural networks on the 5-bit parity classification task. The experiments compare the algorithms when they use different combinations of random number distributions, variations in the step size and changes of the neural networks´ initial weight distribution. A hill climbing algorithm which uses inline search is proposed. In most experiments on the 5-bit parity task it performed better than simulated annealing and standard hill climbing
Keywords :
evolutionary computation; learning (artificial intelligence); neural nets; pattern classification; random number generation; 5-bit parity classification task; 5-bit parity task; artificial neural networks; hill climbing algorithms; initial weight distribution; inline search; neural network training; random number distributions; simulated annealing; step size; Artificial neural networks; Backpropagation algorithms; Computer architecture; Computer networks; Evolutionary computation; Feedforward neural networks; Feedforward systems; Machine learning algorithms; Neural networks; Random variables;
Conference_Titel :
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5536-9
DOI :
10.1109/CEC.1999.785522
