A resource-allocating network for function interpolation
Neural Computation
Prediction of Chaotic Time-Series with a Resource-Allocating RBF Network
Neural Processing Letters
Analysis of the Functional Block Involved in the Design of Radial Basis Function Networks
Neural Processing Letters
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
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
Expert Mutation Operators for the Evolution of Radial Basis Function 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
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
MultiObjective Evolutionary Algorithms (MOEAs) may cause a premature convergence if the selective pressure is too large, so, MOEAs usually incorporate a niche-formation procedure to distribute the population over the optimal solutions and let the population evolve until the Pareto-optimal region is completely explored. This niche-formation scheme is based on a distance index that measures the similarity between two solutions in order to decide if both may share the same niche or not. The similarity criterion is usually based on a Euclidean norm (given that the two solutions are represented with a vector), nevertheless, as this paper will explain, this kind of metric is not adequate for RBFNNs, thus being necessary a more suitable distance index. The experimental results obtained show that a MOEA including the proposed distance index is able to explore sufficiently the Pareto-optimal region and provide the user a wide variety of Pareto-optimal solutions.