Genetic algorithm is searching and optimization technique based on the theory of evolution. Genetic algorithm based upon the principle of survival of fittest. More fit individual has more chance to select for mating. Researcher has done a lot of work in selection phase of Genetic algorithm. In this paper researcher has compare the performance of two selection technique i.e. roulette wheel selec...

A good selection of the internal parameters of a genetic algorithm may reduce the optimization speed considerably. We studied the effects of two key parameters, namely population size and relative elitism, on the optimization speed and reliability. We found that changing one parameter can be compensated for changing another. By penalizing for the unreliability of the genetic algorithm the optim...

This paper describes two elitism-based compact genetic algorithms (cGAs)—persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient compact-type GAs by treating them as estimation of distribution algorithms (EDAs) for solving difficult optimization problems without compromising on memory and computation cos...

In this study, a bi-objective multi-state redundancy allocation problem of series-parallel systems consisting of some serial subsystems, each with non-repairable components in parallel, is investigated. Furthermore, due to uncertainty involved, both the performance rates and the availabilities of components are considered fuzzy. In addition, two strategies of all-unit and incremental-quantity d...