Learning automata: an introduction
Learning automata: an introduction
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Absorbing Stochastic Estimator Learning Algorithms with High Accuracy and Rapid Convergence
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new class of ε-optimal learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimization based on a team of automata with binary outputs
Automatica (Journal of IFAC)
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A global optimization algorithm operating online in a stochastic multi-teacher environment is suggested. An application example introduces a new perspective for solving some optimization problems dealing with reliability. First, a hybrid scheme combining reinforcement-based learning automata and confidence probabilistics is developed for a single-teacher environment. The scheme is able to find the optimal solution with high confidence, yet providing a sequence of search actions that converge to the minimal loss. In addition, the suggested approach provides an on-line measure of the confidence to the current solution. Second, a multi-teacher environment is considered. A simple application of a database enables any single-teacher reinforcement algorithm to be used for updating the learning automaton action probability distribution. Two alternative approaches are suggested, where the former provides superior performance in terms of confidence and loss; the latter is able to deal with dependencies between the cost and the duration of the evaluation of the cost function. The performance of the learning schemes is studied in simulations on maintenance optimization, where an accumulated number of failures is optimized online for a deteriorating production system with preventive maintenance. The simulations indicate superior performance of the hybrid scheme. A significant speed-up is observed by taking advantage of information from processes running online in parallel, thus making the learning automata approach a much more feasible approach for solving engineering problems of practical interest.