Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
An Efficient Fuzzy C-Means Clustering Algorithm
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Asymptotic statistical theory of overtraining and cross-validation
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
A Takagi-Sugeno type neuro-fuzzy network for determining child anemia
Expert Systems with Applications: An International Journal
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In this paper, we propose a new learning algorithm that can be used to design TSK-type neuro-fuzzy networks. Though there has been a great deal of interest in the use of immune algorithms (IAs) for computer science and engineering, in terms of fundamental methodologies, they are not dramatically different from other algorithms. In order to enhance the IA performance, we propose the immune-based symbiotic particle swarm optimization (ISPSO) for use in TSK-type neuro-fuzzy networks for solving the prediction and skin color detection problems. The proposed ISPSO embeds the symbiotic evolution scheme in an IA and utilizes particle swarm optimization (PSO) to improve the mutation mechanism. In order to avoid trapping in a local optimal solution and to ensure the search capability of a near global optimal solution, mutation plays an important role. Therefore, we employed the advantages of PSO to improve the mutation mechanism and used a method that introduces chaotic mapping with certainty, ergodicity and the stochastic property into PSO to improve global convergence. Unlike the IA that uses each individual in a population as a full solution to a problem, symbiotic evolution assumes that each individual in a population represents only a partial solution to a problem. Complex solutions combine several individuals in the population.