Genetic algorithms for optimal image enhancement
Pattern Recognition Letters
Selection of optimal set of weights in a layered network using genetic algorithms
Information Sciences—Intelligent Systems: An International Journal
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Convergence Criteria for Genetic Algorithms
SIAM Journal on Computing
Genetic algorithms for optimality of data hiding in digital images
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Bio-Inspired Information Hiding; Guest editors: Jeng-Shyang Pan, Ajith Abraham
Gray-scale image enhancement as an automatic process driven by evolution
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Convergence analysis of canonical genetic algorithms
IEEE Transactions on Neural Networks
ε - Optimal Stopping Time for Genetic Algorithms
Fundamenta Informaticae
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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.