Reza Rastegar

A new high dynamic range moduli set with efficient reverse converter

The Residue Number System (RNS) is a representation system which provides fast and parallel arithmetic. It has a wide application in digital signal processing and provides enhanced fault tolerance capabilities. In this work, we consider the 3-moduli set {2^n, 2^2^n-1, 2^2^n+1} and propose its residue to binary converter using the Chinese Remainder Theorem. We present its simple hardware implementation that is mainly composed of one Carry Save Adder (CSA), a 4n bit modulo 2^4^n-1 adder, and a few gates. We compare the performance and area utilization of our reverse converter to the reverse converters of the moduli sets { 2^n-1, 2^n, 2^n+1, 2^2^n+1} and {2^n-1, 2^n, 2^n+1, 2^n-2^(^n^+^1^)^/^2+1, 2^n+2^(^n^+^1^)^/^2+1} that have the same dynamic range and we demonstrate that our reverse converter is better in terms of performance and area utilization.

A step forward in studying the compact genetic algorithm

The compact Genetic Algorithm (cGA) is an Estimation of Distribution Algorithm that generates offspring population according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The cGA only needs a small amount of memory; therefore, it may be quite useful in memory-constrained applications. This paper introduces a theoretical framework for studying the cGA from the convergence point of view in which, we model the cGA by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then, we prove that the corresponding ODE converges to local optima and stays there. Consequently, we conclude that the cGA will converge to the local optima of the function to be optimized.

A new discrete binary particle swarm optimization based on learning automata

The particle swarm is one of the most powerful methods for solving global optimization problems. This method is an adaptive algorithm based on social-psychological metaphor. A population of particle adapts by returning stochastically toward previously successful regions in the search space and is influenced by the successes of their topological neighbors. In this paper we propose a learning automata based discrete binary particle swarm algorithm. In the proposed algorithm the set of learning automata assigned to a particle may be viewed as the brain of the particle determining its position from its own and other particles past experience. Simulation results show that the proposed algorithm is a good candidate for solving optimization problems.

Letters: The Population-Based Incremental Learning Algorithm converges to local optima

Here, we propose a convergence proof for the Population-Based Incremental Learning (PBIL). First, we model the PBIL by a Markov process and approximate its behavior using an Ordinary Differential Equation (ODE). Then we prove that the corresponding ODE does not have any stable stationary point in the configuration space except the local maxima of the function to be optimized. Finally, we show that the ODE and consequently the PBIL converge to one of these stable points.

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

In this paper a new evolutionary algorithm, called the CLA-EC (Cellular Learning Automata Based Evolutionary Computing), is proposed. This algorithm is a combination of evolutionary algorithms and the Cellular Learning Automata (CLA). In the CLA-EC each genome string in the population is assigned to one cell of the CLA, which is equipped with a set of learning automata. Actions selected by the learning automata of a cell determine the genome 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. Each learning automaton in the cell updates its internal structure according to a learning algorithm and the received signal vector. The processes of action selection and updating the internal structures of learning automata are repeated until a predetermined criterion is met. To show the efficiency of the proposed model, to solve several optimization problems including real valued function optimization and data clustering problems.

A new evolutionary computing model based on cellular learning automata

In this paper, a new evolutionary computing model, called CLA-EC, is proposed. This new model is a combination of a model called cellular learning automata (CLA) and the evolutionary model. In this new model, each genome is assigned to a cell of cellular learning automata to each of which a set of learning automata is assigned. The set of actions selected by the set of automata associated to a cell determines the genomes string for that cell. Based on a local rule, a reinforcement signal vector is generated and given to the set learning automata residing in the cell. Based on the received signal, each learning automaton updates its internal structure according to a learning algorithm. The process of action selection and updating the internal structure is repeated until a predetermined criterion is met. This model can be used to solve optimization problems. To show the effectiveness of the proposed mode! it has been used to solve several optimization problems such as real valued function optimization and clustering problems. Computer simulations have shown the effectiveness of this model.

A Convergence Proof for the Population Based Incremental Learning Algorithm

Here we propose a convergence proof for the population based incremental learning (PBIL). In our approach, first, we model the PBIL by the Markov process and approximate its behavior using Ordinary Differential Equation (ODE). Then we prove that the corresponding ODE doesn’t have any stable stationary points in [0,1]n, n is the number of variables, except the local maxima of the function to be optimized. Finally we show that this ODE and consequently the PBIL converge to one of these stable attractors.

A fuzzy clustering algorithm using cellular learning automata based evolutionary algorithm

In this paper, a new fuzzy clustering algorithm that uses cellular learning automata based evolutionary computing (CLA-EC) is proposed. The CLA-EC is a model obtained by combining the concepts of cellular learning automata and evolutionary algorithms. The CLA-EC is used to search for cluster centers in such a way that minimizes the clustering criterion. The simulation results indicate that the proposed algorithm produces clusters with acceptable quality with respect to clustering criterion and provides a performance that is superior to that of the C-means algorithm.

A new estimation of distribution algorithm based on learning automata

In this paper we introduce an estimation of distribution algorithm based on a team of learning automata. The proposed algorithm is a model based search optimization method that uses a team of learning automata as a probabilistic model of high quality solutions seen in the search process. Simulation results show that the proposed algorithm is a good candidate for solving optimization problems.

A study on the global convergence time complexity of estimation of distribution algorithms

The Estimation of Distribution Algorithm is a new class of population based search methods in that a probabilistic model of individuals are estimated based on the high quality individuals and used to generate the new individuals. In this paper we compute 1) some upper bounds on the number of iterations required for global convergence of EDA 2) the exact number of iterations needed for EDA to converge to global optima.