Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

The Breeder Genetic Algorithm BGA models arti cial selection as performed by human breeders The science of breeding is based on advanced statistical methods In this paper a connection between genetic algorithm theory and the science of breeding is made We show how the response to selection equation and the concept of heritability can be applied to predict the behavior of the BGA Selection recom...

The breeder genetic algorithm BGA depends on a set of control parameters and genetic operators. In this paper it is shown that strategy adaptation by competing subpopulations makes the BGA more robust and more eecient. Each subpopulation uses a diierent strategy which competes with other subpopulations. Numerical results are presented for a number of test functions.

Electrohydraulic servo system has been used in industry in a wide number of applications. Its dynamics are highly nonlinear and also have large extent of model uncertainties and external disturbances. In order to in-crease the reliability, controllability and utilizing the superior speed of response achievable from electrohydraulic systems, further research is required to develop a control soft...

Random generators or stochastic engines are a key component in the structure of metaheuristic algorithms. This work investigates the effects of non-Gaussian stochastic engines on the performance of metaheuristics when solving a real-world optimization problem. In this work, the bacteria foraging algorithm (BFA) was employed in tandem with four random generators (stochastic engines). The stochas...

This paper develops an optimization procedure for tuning of power systems stabilizer (PSS) and Thyristor controlled Series capacitor (TCSC) based the power oscillation damping controller (POD)n to enhance Power System stability. Bacterial foraging algorithm (BFA) tries to minimize an Eigen value based multi objective function in which some loading conditions are considered concurrently to ensur...

The dynamic behavior of mutation and crossover is investigated with the Breeder Genetic Algorithm. The main emphasis is on binary functions. The genetic operators are compared near their optimal performance. It is shown that mutation is most eecient in small populations. Crossover critically depends on the size of the population. Mutation is the more robust search operator. But the BGA combines...

The dynamic behavior of mutation and crossover is investigated with the Breeder Genetic Algorithm The main emphasis is on binary functions The genetic operators are compared near their optimal performance It is shown that mutation is most e cient in small populations Crossover critically depends on the size of the population Mutation is the more robust search operator But the BGA combines the t...

-The most important task in digital image processing is image segmentation. This paper put forward an unique image segmentation algorithm that make use of a Markov Random Field (MRF) hybrid with biologically inspired technique Bacteria Foraging Optimization Algorithm (BFOA) for Brain Magnetic Resonance Images The proposed new algorithm works on the image pixel data and a region/neighborhood map...