An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Introducing Multi-objective Optimization in Cooperative Coevolution of Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
RBF Neural Networks, Multiobjective Optimization and Time Series Forecasting
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Evolutionary Optimization of RBF Networks
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A multi-objective approach to RBF network learning
Neurocomputing
Simultaneous optimization of weights and structure of an RBF neural network
EA'05 Proceedings of the 7th international conference on Artificial Evolution
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
A prediction approach to support alternative design decision for component-based system development
SEPADS'12/EDUCATION'12 Proceedings of the 11th WSEAS international conference on Software Engineering, Parallel and Distributed Systems, and proceedings of the 9th WSEAS international conference on Engineering Education
Intelligent control of a constant turning force system with fixed metal removal rate
Applied Soft Computing
A case study of muscle dysmorphia disorder diagnostics
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A threshold fuzzy entropy based feature selection for medical database classification
Computers in Biology and Medicine
CAPSO: Centripetal accelerated particle swarm optimization
Information Sciences: an International Journal
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This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant multi-objective particle swarm optimization (TVMOPSO) of radial basis function (RBF) network for diagnosing the medical diseases. This study applied RBF network training to determine whether RBF networks can be developed using TVMOPSO, and the performance is validated based on accuracy and complexity. Our approach is tested on three standard data sets from UCI machine learning repository. The results show that our approach is a viable alternative and provides an effective means to solve multi-objective RBF network for medical disease diagnosis. It is better than RBF network based on MOPSO and NSGA-II, and also competitive with other methods in the literature.