Optimizing genetic operator rates using a markov chain model of genetic algorithms
Fatemeh Vafaee, György Turán, Peter C. Nelson · 2010
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.