Title :
Learning by probabilistic Boolean networks
Author_Institution :
Int. Comput. Sci. Inst., Berkeley, CA, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
Boolean networks, in spite of their structural simplicity, seem to be able to simulate the dynamics of complex biological and nonbiological systems. Learning algorithms in neural networks have shown to be a very promising approach to some problems connected to artificial intelligence. Positive feedback has been successfully used by the genetic algorithm and the ant system. In this paper we propose an adaptive Boolean network that takes advantage of all these properties
Keywords :
Boolean functions; learning (artificial intelligence); neural nets; probability; ant system; artificial intelligence; complex system dynamics simulation; genetic algorithm; learning algorithms; positive feedback; probabilistic Boolean networks; Artificial intelligence; Artificial neural networks; Biological information theory; Biological neural networks; Biological system modeling; Biology; Computer networks; Feedback; Genetic algorithms; Learning;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374297
