Based on negative correlation learning  and evolutionary learning, evolutionary ensembles with negative correlation learning (EENCL) was proposed for learning and designing of neural network ensembles  The idea of EENCL is to regard the population of neural networks as an ensemble, and the evolutionary process as the design of neural network ensembles. EENCL used a fitness sharing based on the covering set. Such fitness sharing did not make accurate measurement on the similarity in the population. In this paper, a fitness sharing scheme based on mutual information is introduced in EENCL to evolve a diverse and cooperative population. The effectiveness of such evolutionary learning approach was tested on two real-world problems.
|Title of host publication
|GECCO'02: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation
|W. B. LANGDON, E. CANTÚ-PAZ, K. MATHIAS, R. ROY, D. DAVIS
|Morgan Kaufmann Publishers, Inc.
|Number of pages
|Published - 9 Jul 2002
|4th Annual Conference on Genetic and Evolutionary Computation - New York City, United States
Duration: 9 Jul 2002 → 13 Jul 2002
|4th Annual Conference on Genetic and Evolutionary Computation
|New York City
|9/07/02 → 13/07/02