Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adapting operator probabilities in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Strategy Adaption by Competing Subpopulations
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Toward the Optimization of a Class of Black Box Optimization Algorithms
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
The impact of the mutation strategy on the quality of solution of parallel genetic algorithms
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Combining evolutionary and stochastic gradient techniques for system identification
Journal of Computational and Applied Mathematics
Advanced Engineering Informatics
Using performance fronts for parameter setting of stochastic metaheuristics
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Reinforcement learning for online control of evolutionary algorithms
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
An exploration into dynamic population sizing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A Neural-Network Controlled Dynamic Evolutionary Scheme for Global Molecular Geometry Optimization
International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
The use of reputation as noise-resistant selection bias in a co-evolutionary multi-agent system
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An adaptive evolutionary approach for real-time vehicle routing and dispatching
Computers and Operations Research
Is the meta-EA a viable optimization method?
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
A meta-GA (GA within a GA) is used to investigate evolving the parameter settings of genetic operators for genetic and evolutionary algorithms (GEA) in the hope of creating a self-adaptive GEA. We report three findings. First, the meta-GA can adapt its genetic operators to different problems and thereby perform well on average across diverse problems. Second, the meta-GA can change its parameters during the course of a run—seemingly a good idea—but this behavior may actually decrease performance. Finally, the genetic operator configurations the meta-GA evolves are far from optimal. We conclude that, while meta-GAs show promise for automating some parameter configurations, they are not likely to replace manually configured genetic and evolutionary algorithms without innovative alteration.