Xin Yao's Research Interests: Automatic Modularisation
It is very hard to design a monolithic learning system (e.g., neural network)
for a large and complex task. The divide-and-conquer strategy needs to be used
in order to decompose a large and complex problem into smaller components and
then solve them separately. However, decomposing a problem requires a lot of
domain knowledge which may not be available or too costly to obtain for many
real-world problems. I am interested in automatic decomposition of learning
systems, especially using evolutionary approaches.
At present, I am interested in the design of neural network
ensembles in which individual networks cooperate with each other to solve a
complex problem. While trying to solve the complex problem, each individual
also tries to be different from other individuals so that it will learn
something that has not been learned correctly by others. A number of
techniques, such as artificial speciation and
negative correlation, will be
studied in this project.
- X. Yao and Y. Liu, ``Making use of population information in evolutionary
artificial neural networks,'' IEEE Transactions on Systems, Man and
Cybernetics, Part B: Cybernetics, 28(3):417-425, June 1998.
- P. J. Darwen and X. Yao, ``Speciation as automatic categorical
modularization,'' IEEE Transactions on Evolutionary
Computation, 1(2):101-108, 1997.
- X. Yao, Y. Liu and P. Darwen,
``How to make
best use of evolutionary learning,'' Complexity International:
An Electronic Journal of Complex Systems Research (ISSN 1320-0682),
Vol. 3, July 1996.
Also appeared in paper form in Complex Systems --- From Local
Interactions to Global Phenomena, IOS Press, Amsterdam, pp.229--242, 1996.
- P. Darwen and X. Yao (1996a), ``Automatic modularisation by speciation,''
Proc. of the Third IEEE International Conference on
Evolutionary Computation (ICEC'96), Nagoya, Japan, 20-22 May 1996,
Last update: 5 September 2000