Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Toward an extrapolation of the simulated annealing convergence theory onto the simple genetic algorithm144438
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Adapting Operator Probabilities in Genetic Algorithms
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
| Hi-index | 0.00 |
Identifying the optimal settings for crossover probability pc and mutation probability pm is of an important problem to improve the convergence performance of GAs. In this paper, we modelled genetic algorithms as controlled Markov chain processes, whose transition depend on control parameters (probabilities of crossover and mutation). A stochastic optimization problem is formed based on the performance index of populations during the genetic search, in order to find the optimal values of control parameters so that the performance index is maximized. We have shown theoretically the existence of the optimal control parameters in genetic search and proved that, for the stochastic optimization problem, there exists a pure deterministic strategy which is at least as good as any other pure or mixed (randomized) strategy.