clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...

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...

Aiming at improving search efficiency limitations of canonical real coded genetic algorithm, this paper improves three aspects for the canonical real coded genetic algorithm, that are initial population generating, overall process of algorithm and the mutation operator, then puts forward an improved real coded genetic algorithm. This improved algorithm combines the series operation and parallel...

The evolution scheme phase, in which the genetic algorithms select individuals that will form new population, had an important impact on these algorithms. Many approaches exist literature. However, consider only value of fitness function to differenciate best solutions from worst ones. This article introduces parenting fitness, a novel parameter, defines capacity individual produce fittest offs...

In our recent research, we showed results of the comparative study on effects of using several kinds of scheduling evaluation criteria as the fitness function of a genetic algorithm for job-shop scheduling. From these results, we obtained that the idle time criterion sometimes can provide a good makespan-minimizing schedule for a job-shop scheduling problem. In this paper, according to the abov...

A novel approach of optimized routing which is based on Genetic Algorithm has been introduced. It finds out the optimum and the most suitable path which qualifies Objective Function criteria. Optimization Approaches have been applied to various real life issues in communication and networking. The path which is best fitted in the population is considered as the optimal path. It is obtained afte...

Interactive genetic algorithms (IGAs) are effective methods to solve optimization problems with implicit or fuzzy indices. But human fatigue problem, resulting from evaluation on individuals and assignment of their fitness, is very important and hard to solve in IGAs. Aiming at solving the above problem, an interactive genetic algorithm with an individual fitness not assigned by human is propos...

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...