Abstract
This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.
| Language | English |
|---|---|
| Title of host publication | 2005 IEEE Congress on Evolutionary Computation - Proceedings |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| Pages | 1262-1267 |
| Number of pages | 6 |
| ISBN (Print) | 0780393635 |
| DOIs | |
| Publication status | Published - 12 Dec 2005 |
| Event | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom Duration: 2 Sep 2005 → 5 Sep 2005 |
Conference
| Conference | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 |
|---|---|
| Country | United Kingdom |
| City | Edinburgh, Scotland |
| Period | 2/09/05 → 5/09/05 |
Fingerprint
Keywords
- optimization
- kriging approximation
- aerodynamics
Cite this
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Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control. / D'Angelo, S.; Minisci, E.A.
2005 IEEE Congress on Evolutionary Computation - Proceedings. Piscataway, NJ : IEEE, 2005. p. 1262-1267.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control
AU - D'Angelo, S.
AU - Minisci, E.A.
PY - 2005/12/12
Y1 - 2005/12/12
N2 - This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.
AB - This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (small size of the database), the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.
KW - optimization
KW - kriging approximation
KW - aerodynamics
UR - http://www.scopus.com/inward/record.url?scp=27144461976&partnerID=8YFLogxK
U2 - 10.1109/CEC.2005.1554835
DO - 10.1109/CEC.2005.1554835
M3 - Conference contribution book
SN - 0780393635
SP - 1262
EP - 1267
BT - 2005 IEEE Congress on Evolutionary Computation - Proceedings
PB - IEEE
CY - Piscataway, NJ
ER -