Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
When Both Individuals and Populations Search: Adding Simple Learning to the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
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We derive the value of the mutation probability which maximizes the probability that the genetic algorithm finds the optimum value of the objective function under simple assumptions. This value is compared with the optimum mutation probability derived in other studies. An empirical study shows that this value, when used with a larger scaling factor in linear scaling, improves the performance of the genetic algorithm. This feature is then added to a model developed by Hinton and Nowlan which allows certain bits to be guessed in an effort to increase the probability of finding the optimum solution.