Learning automata: an introduction
Learning automata: an introduction
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Structure and Performance of Fine-Grain Parallelism in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Explicit Parallelism of Genetic Algorithms through Population Structures
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
IEEE Transactions on Evolutionary Computation
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic learning automata for function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cellular learning automata with multiple learning automata in each cell and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Binarization based edge detection using universal law of gravity and ant colony optimization
International Journal of Hybrid Intelligent Systems
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In this paper a new evolutionary algorithm, called the CLA-EC (Cellular Learning Automata Based Evolutionary Computing), is proposed. This algorithm is a combination of evolutionary algorithms and the Cellular Learning Automata (CLA). In the CLA-EC each genome string in the population is assigned to one cell of the CLA, which is equipped with a set of learning automata. Actions selected by the learning automata of a cell determine the genome string for that cell. Based on a local rule, a reinforcement signal vector is generated and given to the set of learning automata residing in the cell. Each learning automaton in the cell updates its internal structure according to a learning algorithm and the received signal vector. The processes of action selection and updating the internal structures of learning automata are repeated until a predetermined criterion is met. To show the efficiency of the proposed model, to solve several optimization problems including real valued function optimization and data clustering problems.