.png){kind=icon}
.png){kind=icon}
Return
Cultured differential evolution for constrained optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cma.2005.09.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A cultural algorithm with a differential evolution population is proposed in this paper. This cultural algorithm uses different knowledge sources to influence the variation operator of the differential evolution algorithm, in order to reduce the number of fitness function evaluations required to obtain competitive results. Comparisons are provided with respect to three techniques that are representative of the state-of-the-art in the area. The results obtained by our algorithm are similar (in quality) to those obtained by the other approaches with respect to which it was compared. However, our approach requires a lower number of fitness function evaluations than the others. (c) 2005 Elsevier B.V. All rights reserved.
Keywords:
evolutionary algorithms
optimization
differential evolution cultural algorithms
evolutionary optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.3
Papers:
1.3W
Citations:
5.6W
Organization
No organization information available