Empirical model-building and response surface
Empirical model-building and response surface
Learning automata: an introduction
Learning automata: an introduction
Local and global optimization algorithms for generalized learning automata
Neural Computation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Learning Algorithms Theory and Applications
Learning Algorithms Theory and Applications
Efficient fast learning automata
Information Sciences—Informatics and Computer Science: An International Journal
Active learning with statistical models
Journal of Artificial Intelligence Research
Learning automata based classifier
Pattern Recognition Letters
| Hi-index | 0.07 |
This paper presents a new method for optimizing continuous complex functions based on a learning automaton. This method can be considered as active learning permitting to select on-line the most significant data samples in order to quickly converge to a quasi global optimum of the functions to be optimized with a fewer number of tests or calculations. Like other stochastic optimization algorithms, it aims at finding a compromise between exploitation and exploration, i.e. converging to the nearest local optima and exploring the function behavior in order to discover global optimal regions. During the optimization procedure, this method enhances local search in interesting regions or intervals and reduces the whole searching space by removing useless regions or intervals.