Name
Abstract | ||
|---|---|---|
The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HAEA a random parameter control. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CEC.2007.4424498 | 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS |
Keywords | Field | DocType |
evolutionary algorithm,reinforcement learning | Evolutionary acquisition of neural topologies,Mathematical optimization,Evolutionary algorithm,Computer science,Artificial intelligence,Evolutionary programming,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Montero | 1 | 69 | 10.14 |
| María Cristina Riff | 2 | 200 | 23.91 |