Optimizing genetic operator rates using a markov chain model of genetic algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Lévy-Flight genetic programming: towards a new mutation paradigm
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Adaptive directed mutation for real-coded genetic algorithms
Applied Soft Computing
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Dealing with many free parameters and finding an appropriate set of parameter values for an evolutionary algorithm (EA) has been a longstanding major challenge of the Evolutionary Computation community. Such difficulty has directed researchers' attention towards devising an automated ways of controlling EA parameters. This work is concerned with proposing a novel method which adaptively adjusts EA and specifically genetic algorithm (GA) mutation rates. The proposed method incorporates the underlying statistical framework of biological evolutionary models into the generic context of evolutionary algorithms. By using such model, besides adapting the mutation rate, this method aims to wisely determine the types of replacing genes in the mutation procedure. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a wide array of test functions and the outcome is compared with a state-of-the-art adaptive mutation evolutionary algorithm. The results demonstrates that the newly suggested algorithm significantly outperform its adaptive rival in most of the test cases.