Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An introduction to fuzzy control
An introduction to fuzzy control
Applicability of the fuzzy operators in the design of fuzzy logic controllers
Fuzzy Sets and Systems
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Selected papers from the IEEE/Nagoya-University World Wisepersons Workshop on Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms,
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Adapting operator settings in genetic algorithms
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
A problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of population diversity. The mutation operator is the one responsible for the generation of diversity and therefore may be considered to be an important element in solving this problem. A solution adopted involves the control, throughout the run, of the parameter that determines its operation: the mutation probability. In this paper, we study an adaptive approach for the control of the mutation probability based on the application of fuzzy logic controllers. Experimental results show that this technique consistently outperforms other mechanisms presented in the genetic algorithm literature for controlling this genetic algorithm parameter.