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...

In this work, we propose a method based on Genetic algorithm hybridized with Pattern Search for joint estimation of Amplitude and Direction of Arrival, azimuth as well as elevation angles using L-type array. Four other schemes, i.e., the Genetic algorithm, Pattern Search, Simulated Annealing and Simulated Annealing hybridized with Pattern Search are also discussed and compared with Genetic algo...

A new recombination operator, called Gene Pool Recombination (GPR) is introduced. In GPR, the genes are randomly picked from the gene pool defined by the selected parents. The mathematical analysis of GPR is easier than for two-parent recombination (TPR) normally used in genetic algorithms. There are n difference equations for the marginal gene frequencies that describe the evolution of a popul...

In present study, a three-step multi-objective optimization algorithm of cyclone separators is catered for the design objectives. First, the pressure drop (Dp) and collection efficiency (h) in a set of cyclone separators are numerically evaluated. Secondly, two meta models based on the evolved Group Method of Data Handling (GMDH) type neural networks are regarded to model the Dp and h as the re...

In this paper, optimal placement of Phasor Measurement Unit (PMU) using Global Positioning System (GPS) is discussed. Ant Colony Optimization (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modifi...

controller design and optimization problems, with more than one objective, are referred as multiple objectives or multiple attributed problems. in this paper, a novel method is proposed for designing optimum pid controller that is called genetic multiple attributed decision making method (gmadm). this method is newer than the previous methods and in this paper some options are considered that h...

Composing drum patterns and musically developing them through repetition and variation is a typical task in electronic music production. We propose a system that, given an input pattern, automatically creates related patterns using a genetic algorithm. Two distance measures (the Hamming distance and directed-swap distance) that relate to rhythmic similarity are shown to derive usable fitness fu...

The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems. This dissertation describes a series of theoretical and experimental studies on a variety of evolutionar...

In this paper, a new approach to synthesize bent Boolean functions by means of Cartesian Genetic Programming (CGP) is proposed. Bent functions have important applications in cryptography due to their high nonlinearity. However, they are very rare and their discovery using conventional brute force methods is not efficient enough. We show that by using CGP we can routinely design bent functions o...

We introduce a new parameter to discuss the behavior of a genetic algorithm. This parameter is the mean number of exact copies of the best fit chromosomes from one generation to the next. We argue that the genetic algorithm should operate efficiently when this parameter is slightly larger than 1. We consider the case of the simple genetic algorithm with the roulette–wheel selection mechanism. W...