The aim of this study was using Bayesian approach via Gibbs sampling (GS) for estimating genetic parameters of production, reproduction and health traits in Iranian Holstein cows. Data consisted of 320666 first- lactation records of Holstein cows from 7696 sires and 260302 dams collected by the animal breeding center of Iran from year 1991 to 2010. (Co) variance components were estimated using ...

Genetic Algorithm (GA) has been successfully applied to many optimization problems. One problem with Standard GA is its premature convergence for complex multi-modal functions. To overcome it, in this paper a novel genetic algorithm with age and sexual features is proposed. Age and sexual features are provided to individuals to simulate the sexual reproduction popular in nature. During applying...

Genetic algorithm is an evolutionary algorithm. It is particularly suitable for solving NP-complete optimization problems. In this paper, we propose a rapid genetic algorithm based on chaos mechanism. We introduce the chaos mechanism into the genetic algorithm. Using the ergodic property of the chaos movement, this method can remedy the defect of premature convergence in the genetic algorithm. ...

The fundamental problem in genetic algorithms is premature convergence, and it is strongly related to the loss of genetic diversity of the population. This study aims at proposing some techniques to tackle the premature convergence by controlling the population diversity. Firstly, a sexual selection mechanism which utilizes the mate chromosome during selection is used. The second technique focu...

the imperialist competitive algorithm (ica) that was recently introduced has shown its good performance in optimization problems. this algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. this paper presents optimization of bead geometry in welding process using of ica. therefore, two case studies from literature are presented t...

Most techniques for solving dynamic optimization problems involve a series of gradient computations and one-dimensional searches at some point in the optimization process. A large class of problems, however, does not possess the necessary smoothness properties that such algorithms require for good convergence. Even when smoothness conditions are met, poor initial guesses at the solution often r...

To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional Genetic Algorithm (GA) to handle complex functions, a novel compound evolutionary algorithm, namely, Multiple Population Genetic Evolutionary Optimization Algorithm (MPGA) , was introduced and selecting parameters are given. MPGA algorithm program was also developed. The computing example ...

In our recent paper [9], we proposed a new hybrid genetic algorithm (NHGA) for the job shop scheduling problems. The method of encoding we used is Natural coding instead of traditional binary coding. This manner of coding has a lot of advantages but its convergence is still an open issue for years. This paper analyzes the convergence property of the NHGA by applying properties of Markov chains....

real coded genetic algorithm, rcga, is the type of ga which operates on chromosomes with real valued parameters. different mutation and crossover operations are defined for rcga. one usable crossover for this kind of ga is to consider its chromosomes simply as bit strings and utilize the same operations as binary coded ga. in this paper, we attempt to show that this kind of crossover can not ha...

–Parameter convergence in Genetic Algorithm (GA) is quiet unpredictable since it is not tied with the number of episodes (i.e., the number of generations), convergence time, or any other parameter it require to converge. Thus, in such a situation, trial to frame out any hypothesis to predict definite number of episodes gets disapproved; in turn, the alternate hypothesis becomes acceptable that ...