A New Evolutionary Model Based on Cellular Learning Automata and Chaos Theory

In this paper, a new fine-grained evolutionary model, called CCLA-EM, is proposed for solving the optimization problems, which greatly overcomes the premature convergence problem of the existing evolutionary algorithms. In the proposed model, a combination of an evolutionary algorithm with a cellular learning automaton is used. The population individuals are distributed on the cells of a cellular learning automaton. Each individual interacts and cooperates with the individuals of neighboring cells to reach the global optimum. Distributing the population individuals on the cells of a cellular learning automaton allows the parallel implementation of the proposed model. Also, in different stages of the proposed model, numbers generated by a chaotic process are used instead of random ones. The use of numbers generated by a chaotic process leads to a complete search of the search space and hence avoids being trapped in local optima. Experiments on various benchmarks of the community structure detection problem indicate the superiority of the proposed model to the well-known algorithms GA-net and ICLA-net.