The cellular learning automata, which is a combination of cellular automata and learning automata, is introduced recently. This model is superior to cellular automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata is to use the learning automata to adjust the state transition probability of stochastic cellular automata. Recently, various types of cellular learning automata such as synchronous, asynchronous, and open cellular learning automata have been introduced. In some applications such as cellular networks we need to have a model of cellular learning automata for which multiple learning automata resides in each cell. In this paper, we study a cellular learning automata model for which each cell has several learning automata. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. Two applications of this new model such as channel assignment in cellular mobile networks and function optimization are also given. For both applications, it has been shown through computer simulations that the cellular learning automata based solutions produce better results.

Abstract—The hybridization of artificial immune system with cellular automata (CA-AIS) is a novel method. In this hybrid model, the cellular automaton within each cell deploys the artificial immune system algorithm under optimization context in order to increase its fitness by using its neighbor’s efforts. The hybrid model CA-AIS is introduced to fix the standard artificial immune system’s weaknesses. The credibility of the proposed approach is evaluated by simulations and it shows that the proposed approach achieves better results compared to standard artificial immune system.

Abstract. Uniform fractional policy (UFC) is a call admission policy that accepts new calls with a probability pi. In order to find the optimal value of pi, we need to know all traffic parameters or to estimate them. In this paper, we first propose a new adaptive algorithm based on learning automata for finding the optimal value of UFC parameter and then study its steady state behavior. It is shown that the given adaptive algorithm converges to an equilibrium point which is also optimal for UFC policy. In order to study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that the level of QoS is satisfied by the proposed algorithm and the performance of given algorithm is very close to the performance of uniform fractional guard channel policy which needs to know all parameters of input traffic.

In this paper, we propose an adaptive call admission algorithm based on learning automata. The proposed algorithm uses a learning automaton to specify the acceptance/rejection of incoming new calls. It is shown that the given adaptive algorithm converges to an equilibrium point which is also optimal for uniform fractional channel policy. In order to study the performance of the proposed call admission policy, the computer simulations are conducted. The simulation results show that the level of QoS is satisfied by the proposed algorithm and the performance of given algorithm is very close to the performance of uniform fractional guard channel policy which needs to know all parameters of input traffic.The simulation results also confirm the analysis of the steady-state behaviour.

Abstract- Bayesian Network (BN) is a probabilistic graphical model which describes the joint probability distribution over a set of random variables. Finding an optimal network structure based on an available training dataset is one of the most important challenges in the field of BNs. Since the problem of searching the optimal BN structure belongs to the class of NP-hard problems, typically greedy algorithms are used to solve it. In this paper two novel learning automata-based algorithms are proposed to solve the BNs’ structure learning problem. In both, there is a learning automaton corresponding with each possible edge to determine the appearance and the direction of that edge in the constructed network; therefore, we have a game of learning automata, at each stage of the proposed algorithms. Two special cases of the game of the learning automata have been discussed, namely, the game with a common payoff and the competitive game. In the former, all the automata in the game receive a unique payoff from the environment, but in the latter, each automaton receives its own payoff. As the algorithms proceed, the learning processes focus on the BN structures with higher scores. The use of learning automata has led to design the algorithms with a guided search scheme, which can avoid getting stuck in local maxima. Experimental results show that the proposed algorithms are capable of finding the optimal structure of BN in an acceptable execution time; and compared with other search-based methods, they outperform them.