A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Generalizing the notion of schema in genetic algorithms
Artificial Intelligence
Proceedings of the fourth international conference on Genetic algorithms
Proceedings of the fourth international conference on Genetic algorithms
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
Directed mutation in genetic algorithms
Information Sciences—Intelligent Systems: An International Journal
Genetic algorithms with fuzzy fitness function for object extraction using cellular networks
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Pattern classification with genetic algorithms: incorporation of chromosome differentiation
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
A Markov Chain Analysis on A Genetic Algorithm
Proceedings of the 5th International Conference on Genetic Algorithms
Convergence analysis of canonical genetic algorithms
IEEE Transactions on Neural Networks
Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model
Fundamenta Informaticae
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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.