Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Particle Swarm Optimization of Neural Network Architectures andWeights
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Particle swarm classification: A survey and positioning
Pattern Recognition
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This paper presents a novel and notable swarm approach to evolve an optimal set of weights and architecture of a neural network for classification in data mining. In a distributed environment the proposed approach generates randomly multiple architectures competing with each other while fine-tuning their architectural loopholes to generate an optimum model with maximum classification accuracy. Aiming at better generalization ability, we analyze the use of particle swarm optimization (PSO) to evolve an optimal architecture with high classification accuracy. Experiments performed on benchmark datasets show that the performance of the proposed approach has good classification accuracy and generalization ability. Further, a comparative performance of the proposed model with other competing models is given to show its effectiveness in terms of classification accuracy.