A new learning automata algorithm for selection of optimal subset

Abstract

A new class of learning automata for the purpose of learning the optimal subset of actions has been proposed to fulfill the demand of application such as allocation and global optimization. Learning automata are capable of dealing with multiple choice problems if some modifications are made on current algorithms. This paper discusses on how to adapt current LA algorithms to the new purpose and introduces a new kind of learning automata. The proposed automata take advantage of LELA, whose original updating schemes favor the purpose of selecting multiple actions and thus acquire faster rate of convergence than the existing automata for selecting optimal subset of actions to the best of our knowledge. Additionally, extensive simulation results are presented to compare the performance between the proposed algorithm and the existing ones. The results show that the proposed automata outperform the other automata.