Name
Abstract | ||
|---|---|---|
The control of parameters during the execution of bio-inspired algorithms is an open research area. In this paper, we propose a new parameter control strategy for the immune algorithm CLONALG. Our approach is based on reinforcement learning ideas. We focus our attention on controlling the number of clones. Our approach provides an efficient and low cost adaptive technique for parameter control. We use instances of the Travelling Salesman Problem. The results obtained are very encouraging. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16236-7_3 | Transactions on Computational Science |
Keywords | Field | DocType |
open research area,travelling salesman,low cost adaptive technique,dynamic adaptive strategy,clonalg algorithm,bio-inspired algorithm,parameter control,immune algorithm,new parameter control strategy,reinforcement learning,travelling salesman problem | Open research,Mathematical optimization,Adaptive strategies,Computer science,Algorithm,Travelling salesman problem,Artificial intelligence,Clonal selection algorithm,Parameter control,Genetic algorithm,Reinforcement learning | Journal |
Volume | ISSN | ISBN |
8 | 0302-9743 | 3-642-16235-5 |
Citations | PageRank | References |
3 | 0.38 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Cristina Riff | 1 | 200 | 23.91 |
| Elizabeth Montero | 2 | 69 | 10.14 |
| Bertrand Neveu | 3 | 253 | 23.18 |