Name
Abstract | ||
|---|---|---|
The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and interesting areas of research in evolutionary computation. In this paper we propose two new parameter control strategies for evolutionary algorithms based on the ideas of reinforcement learning. These strategies provide efficient and low-cost adaptive techniques for parameter control and they preserve the original design of the evolutionary algorithm, as they can be included without changing either the structure of the algorithm nor its operators design. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.ins.2010.09.016 | Inf. Sci. |
Keywords | Field | DocType |
on-the-fly calibrating strategy,various parameter,original design,low-cost adaptive technique,parameter control,evolutionary computation,operators design,evolutionary algorithm,interesting area,reinforcement learning,new parameter control strategy,evolutionary computing,evolutionary algorithms | Interactive evolutionary computation,Evolutionary acquisition of neural topologies,Evolutionary algorithm,Human-based evolutionary computation,Computer science,Evolutionary computation,Artificial intelligence,Cultural algorithm,Evolutionary programming,Evolutionary music,Machine learning | Journal |
Volume | Issue | ISSN |
181 | 3 | 0020-0255 |
Citations | PageRank | References |
14 | 0.73 | 27 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Montero | 1 | 69 | 10.14 |
| María Cristina Riff | 2 | 200 | 23.91 |