Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd 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
Cooperative Model for Genetic Operators to Improve GAs
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
In this work we study varying mutations applied either serial or parallel to crossover and discuss its effect on the performance of deterministic and self-adaptive varying mutation GAs. After comparative experiments, we found that varying mutation parallel to crossover can be a more effective framework in both deterministic and self-adaptive GAs to achieve faster convergence velocity and higher convergence reliability. Best performance is achieved by a parallel varying mutation self-adaptive GA.