PREMATURE CONVERGENCE AND GENETIC ALGORITHM UNDER OPERATING SYSTEM PROCESS SCHEDULING PROBLEM
An important assumption to maximize the performance of genetic algorithm is to study the convergence state of genetic algorithm. Genetic algorithm is a Mata-heuristic search technique; this technique is based on the Darwin theory of Natural Selection. The important property of this algorithm is that it has worked on multiple state of solution. This algorithm is work with some finite set of population. The population contains set of individual, which represent the solution. Each member of the population is represented by a string written over fixed alphabets and also each member has a merit value associated with it, which represent its suitability for the problem under consideration. There are many coding techniques have been implemented for genetic algorithm. In this paper we study the effect of crossover and inversion probability on the convergence of genetic algorithm .The convergence of genetic algorithm is depends upon the parameter setting of genetic algorithm.