ε - Optimal Stopping Time for Genetic Algorithms

Article type: Research Article

Authors: Murthy, C.A. | Bhandari, Dinabandhu | Pal, Sankar K.

Affiliations: Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Calcutta 700 035, India sankar@isical.ac.in

Note: [] Address for correspondence: Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Calcutta 700 035, India

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.

DOI: 10.3233/FI-1998-35123406

Journal: Fundamenta Informaticae, vol. 35, no. 1-4, pp. 91-111, 1998