ε - Optimal Stopping Time for Genetic Algorithms

Abstract

In this article, the concept of e-optimal stopping time of a genetic algorithm with elitist model (EGA) has been introduced. The probability of performing mutation plays an important role in the computation of the ε-optimal stopping times. Two approaches, namely, pessimistic and optimistic have been considered here to find out the ε-optimal stopping time. It has been found that the total number of strings to be searched in the optimistic approach to obtain ε-optimal string is less than the number of all possible strings for sufficiently large string length. This observation validates the use of genetic algorithms in solving complex optimization problems.