Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Genetic local search in combinatorial optimization
CO89 Selected papers of the conference on Combinatorial Optimization
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Expert Systems with Applications: An International Journal
A novel multiobjective optimization algorithm based on bacterial chemotaxis
Engineering Applications of Artificial Intelligence
The application of nonlinear structures to the reconstruction ofbinary signals
IEEE Transactions on Signal Processing
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Nonlinear channel equalization for QAM signal constellation usingartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Holographic dispersal and recovery of information
IEEE Transactions on Information Theory
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
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This paper proposes a neuro-fuzzy filter for equalization of time-varying channels. Additionally, it proposes to tune the equalizer with a hybrid algorithm between Genetic Algorithms (GA) and Bacteria Foraging (BFO), termed as GBF. The major advantage of the method developed in this paper is that all parameters of the neuro-fuzzy network, including the rule base, are tuned simultaneously through the proposed hybrid algorithm of genetic Algorithm and bacteria foraging. The performance of the Neuro-Fuzzy equalizer designed using the proposed approach is compared with Genetic algorithm based equalizers. The results confirm that the methodology used in the paper is much better than existing approaches. The proposed hybrid algorithm also eliminates the limitations of GA based equalizer, i.e. the inherent characteristic of GA, i.e. GAs risk finding a sub-optimal solution.