Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Co-evolving draughts strategies with differential evolution
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Specialised Recombinative Operators for Timetabling Problems
Selected Papers from AISB Workshop on Evolutionary Computing
Adapting operator settings in genetic algorithms
Evolutionary Computation
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
Hi-index | 0.00 |
This paper examines two techniques for setting the parameters of an evolutionary Algorithm (EA). The example EA used for test purposes undertakes a simple scheduling problem. An initial version of the EA was tested utilising a set of parameters that were decided by basic experimentation. Two subsequent versions were compared with the initial version, the first of these adjusted the parameters at run time, the second used a set of parameters decided on by running a meta-EA. The authors have been able to conclude that the usage of a meta-EA allows an efficient set of parameters to be derived for the problem EA.