A new fine-grained evolutionary algorithm based on cellular learning automata

In this paper, a new evolutionary computing model, called CLA-EC, is proposed. This model is a combination of a model called cellular learning automata (CLA) and the evolutionary model. In this model, every genome in the population is assigned to one cell of CLA and each cell in CLA is equipped with a set of learning automata. Actions selected by learning automata of a cell determine the genome’s 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. On the basis of the received signal, each learning automaton in the cell updates its internal structure according to a learning algorithm. The process of action selection and updating the internal structure of learning automata is repeated until a predetermined criterion is met. To show the effectiveness of the proposed model it is used to solve several optimization problems such as real valued function optimization and clustering problems.