Evolving an Ensemble of Neural Networks Using Artificial Immune Systems
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
On the significance of the permutation problem in neuroevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Evolving simple feed-forward and recurrent ANNs for signal classification: a comparison
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A successful interdisciplinary course on computational intelligence
IEEE Computational Intelligence Magazine
Coevolution in a large search space using resource-limited nash memory
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Genetic representation and evolvability of modular neural controllers
IEEE Computational Intelligence Magazine
The facilitatory role of linguistic instructions on developing manipulation skills
IEEE Computational Intelligence Magazine
Application notes: robust morphogenesis of robotic swarms
IEEE Computational Intelligence Magazine
Negative correlation learning of neuro-fuzzy system ensembles
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Research frontier: deep machine learning--a new frontier in artificial intelligence research
IEEE Computational Intelligence Magazine
An optimizing BP neural network algorithm based on genetic algorithm
Artificial Intelligence Review
Solving large n-bit parity problems with the evolutionary ANN ensemble
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Unpacking and understanding evolutionary algorithms
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
Automatic Detection of Arrow Annotation Overlays in Biomedical Images
International Journal of Healthcare Information Systems and Informatics
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Using a coordinated group of simple solvers to tackle a complex problem is not an entirely new idea. Its root could be traced back hundreds of years ago when ancient Chinese suggested a team approach to problem solving. For a long time, engineers have used the divide-and-conquer strategy to decompose a complex problem into simpler sub-problems and then solve them by a group of solvers. However, knowing the best way to divide a complex problem into simpler ones relies heavily on the available domain knowledge. It is often a manual process by an experienced engineer. There have been few automatic divide-and-conquer methods reported in the literature. Fortunately, evolutionary computation provides some of the interesting avenues to automatic divide-and-conquer methods. An in-depth study of such methods reveals that there is a deep underlying connection between evolutionary computation and ANN ensembles. Ideas in one area can be usefully transferred into another in producing effective algorithms. For example, using speciation to create and maintain diversity had inspired the development of negative correlation learning for ANN ensembles, and an in-depth study of diversity in ensembles. This paper will review some of the recent work in evolutionary approaches to designing ANN ensembles.