Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A sequential niching technique for particle swarm optimization
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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This research investigates a swarm intelligence based multi-objective optimization algorithm for optimizing the behavior of a group of Artificial Neural Networks (ANNs), where each ANN specializes to solving a specific part of a task, such that the group as a whole achieves an effective solution. Niche Particle Swarm Optimization (NichePSO) is a speciation technique that has proven effective at locating multiple solutions in complex multivariate tasks. This research evaluates the efficacy of the NichePSO method for training a group of ANNs that form a neural network ensemble (NNE) for the purpose of solving a set of multivariate tasks. NichePSO is compared with a gradient descent method for training a set of individual ANNs to solve different parts of a multivariate task, and then combining the outputs of each ANN into a single solution. To date, there has been little research that has compared the effectiveness of applying NichePSO versus more traditional supervised learning methods for the training of neural network ensembles.