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Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model

Bhandari D., Murthy C.A., Pal S.K.

Fundamenta Informaticae

2012

Vol. 120, nr 2

145-164

Genetic Algorithm (GA) has now become one of the leading mechanisms in providing solution to complex optimization problems. Although widely used, there are very few theoretical guidelines for determining when to stop the algorithm. This article establishes theoretically that the variance of the best fitness values obtained in the iterations can be considered as a measure to decide the termination criterion of a GA with elitist model (EGA). The criterion automatically takes into account the inherent characteristics of the objective function. Implementation issues of the proposed stopping criterion are explained. Its difference with some other stopping criteria is also critically analyzed.

E - optimal stopping time for genetic algorithms

Murthy C.A., Bhandari D., Pal S.K.

Fundamenta Informaticae

1998

Vol. 35, nr 1-4

91-111

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 Î-optimal stopping times. Two approaches, namely, pessimistic and optimistic have been considered here to find out the e-optimal stopping time. It has been found that the total number of strings to be searched in the optimistic approach to obtain e-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.