Abstract
We propose a method for approximating a fitness landscape using adaptive support vector regression (SVR) with opposition based learning (OBL) to enhance the evolutionary search. This method tries to resolve the complexity of the fitness landscape in the original search space by designing a suitable kernel function with an adaptive parameter tuned by OBL; This kernel projects the original search space into a higher dimensional search space with a different topological structure. The elite is obtained from the approximated fitness landscape, using the adaptive SVR to accelerate the evolutionary computation (EC) search, and the individual with the worst fitness is replaced. The merits of the proposed method are evaluated by comparing it with the fitness landscape approximated in the original, in a lower and in a higher dimensional search space.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 |
| Pages | 1329-1334 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - Dec 1 2013 |
| Event | 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom Duration: Oct 13 2013 → Oct 16 2013 |
Other
| Other | 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 |
|---|---|
| Country | United Kingdom |
| City | Manchester |
| Period | 10/13/13 → 10/16/13 |
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All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
Cite this
Fitness landscape approximation by adaptive support vector regression with opposition-based learning. / Pei, Yan; Takagi, Hideyuki.
Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. 2013. p. 1329-1334 6721983.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fitness landscape approximation by adaptive support vector regression with opposition-based learning
AU - Pei, Yan
AU - Takagi, Hideyuki
PY - 2013/12/1
Y1 - 2013/12/1
N2 - We propose a method for approximating a fitness landscape using adaptive support vector regression (SVR) with opposition based learning (OBL) to enhance the evolutionary search. This method tries to resolve the complexity of the fitness landscape in the original search space by designing a suitable kernel function with an adaptive parameter tuned by OBL; This kernel projects the original search space into a higher dimensional search space with a different topological structure. The elite is obtained from the approximated fitness landscape, using the adaptive SVR to accelerate the evolutionary computation (EC) search, and the individual with the worst fitness is replaced. The merits of the proposed method are evaluated by comparing it with the fitness landscape approximated in the original, in a lower and in a higher dimensional search space.
AB - We propose a method for approximating a fitness landscape using adaptive support vector regression (SVR) with opposition based learning (OBL) to enhance the evolutionary search. This method tries to resolve the complexity of the fitness landscape in the original search space by designing a suitable kernel function with an adaptive parameter tuned by OBL; This kernel projects the original search space into a higher dimensional search space with a different topological structure. The elite is obtained from the approximated fitness landscape, using the adaptive SVR to accelerate the evolutionary computation (EC) search, and the individual with the worst fitness is replaced. The merits of the proposed method are evaluated by comparing it with the fitness landscape approximated in the original, in a lower and in a higher dimensional search space.
UR - http://www.scopus.com/inward/record.url?scp=84893623121&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893623121&partnerID=8YFLogxK
U2 - 10.1109/SMC.2013.230
DO - 10.1109/SMC.2013.230
M3 - Conference contribution
SN - 9780769551548
SP - 1329
EP - 1334
BT - Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
ER -