A learning automata based algorithm for optimization of continuous complex functions

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.