Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Varying the probability of mutation in the genetic algorithm
Proceedings of the third international conference on Genetic algorithms
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
General Layout of City Pedestrian Bridge
ICIII '08 Proceedings of the 2008 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 03
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
A Genetic Algorithm that Incorporates an Adaptive Mutation Based on an Evolutionary Model
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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This work is concerned with proposing a robust framework for optimizing operator rates of simple Genetic Algorithms (GAs) during a GA run. The suggested framework is built upon a formerly proposed GA Markov chain model to estimate the optimal values of the operator rates based on the time and the current state of the evolution. Though the proposed framework has been formalized for optimizing both mutation and crossover rates, in the current paper, we only implemented it as the mutation rate optimizer and kept the crossover rate constant. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a set of benchmark problems and the outcome is compared with a series of well-known relevant algorithms. The results demonstrate that the newly suggested algorithm significantly outperforms its rivals.