Abstract
| Language | English |
|---|---|
| Pages | 1792-1799 |
| Number of pages | 8 |
| DOIs | |
| State | Published - 6 Jul 2014 |
| Event | CEC 2014: IEEE Conference on Evolutionary computation - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 http://www.ieee-wcci2014.org/ |
Conference
| Conference | CEC 2014 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 6/07/14 → 11/07/14 |
| Internet address |
Fingerprint
Keywords
- adaptive algorithms
- differential evolution
Cite this
}
Adaptive inflationary differential evolution. / Minisci, Edmondo; Vasile, Massimiliano.
2014. 1792-1799 Paper presented at CEC 2014, Beijing, China.Research output: Contribution to conference › Paper
TY - CONF
T1 - Adaptive inflationary differential evolution
AU - Minisci,Edmondo
AU - Vasile,Massimiliano
N1 - .
PY - 2014/7/6
Y1 - 2014/7/6
N2 - In this paper, an adaptive version of Inflationary Differential Evolution is presented and tested on a set of real case problems taken from the CEC2011 competition on realworld applications. Inflationary Differential Evolution extends standard Differential Evolution with both local and global restart procedures. The proposed adaptive algorithm utilizes a probabilistic kernel based approach to automatically adapt the values of both the crossover and step parameters. In addition the paper presents a sensitivity analysis on the values of the parameters controlling the local restart mechanism and their impact on the solution of one of the hardest problems in the CEC2011 test set.
AB - In this paper, an adaptive version of Inflationary Differential Evolution is presented and tested on a set of real case problems taken from the CEC2011 competition on realworld applications. Inflationary Differential Evolution extends standard Differential Evolution with both local and global restart procedures. The proposed adaptive algorithm utilizes a probabilistic kernel based approach to automatically adapt the values of both the crossover and step parameters. In addition the paper presents a sensitivity analysis on the values of the parameters controlling the local restart mechanism and their impact on the solution of one of the hardest problems in the CEC2011 test set.
KW - adaptive algorithms
KW - differential evolution
UR - http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=32438
U2 - 10.1109/CEC.2014.6900587
DO - 10.1109/CEC.2014.6900587
M3 - Paper
SP - 1792
EP - 1799
ER -