Guaranteeing the probability of success using repeated runs of genetic algorithm

Though genetic algorithm (GA) has found widespread application, there appea rs to be no guarantee of success or quantitative measure of the probability of success in a given application. This paper addresses this problem using the notion of repeatedly applying a GA. Several alternative interpretation s of the algorithm are offered. The Q factor is introduced to characterize the efficacy of any GA. The repeated algorithm is applied to a six-degree o bject detection problem and experimental results are reported. A general me thodology is given on the design of GA in a particular problem based on def ining the maximum variation of a problem, using the training set to estimat e the average probability of a single run to the desired level of statistic al confidence, and using the testing set to verify the required performance . This paper paves the way for applying the GA to robust industrial applica tions for which the probability of convergence to the globally correct solu tion is required to be arbitrarily high. (C) 2001 Elsevier Science B.V. All rights reserved.