| Asymmetric quadratic landscape approximation model |
| - |
| Tantar, Alexandru-Adrian  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] |
| Tantar, Emilia [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] |
| Schütze, O. [Computer Science Department, CINVESTAV-IPN, Av. IPN 2508, Col. San Pedro Zacatenco, Mexico City, Mexico] |
| 2014 |
| GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference |
| Association for Computing Machinery |
| 493-500 |
| Yes |
| International |
| 16th Genetic and Evolutionary Computation Conference, GECCO 2014 |
| 12 July 2014 through 16 July 2014 |
| Vancouver, BC |
| [en] Optimization ; Polynomial approximation ; Asymmetric models ; Continuous functions ; In-depth analysis ; Landscape approximation ; Local convexities ; Quadratic approximation ; Robust optimization ; Threshold distances ; Approximation algorithms |
| [en] This work presents an asymmetric quadratic approximation model and an ε-archiving algorithm. The model allows to construct, under local convexity assumptions, descriptors for local optima points in continuous functions. A descriptor can be used to extract confidence radius information. The ε-archiving algorithm is designed to maintain and update a set of such asymmetric descriptors, spaced at some given threshold distance. An in-depth analysis is conducted on the stability and performance of the asymmetric model, comparing the results with the ones obtained by a quadratic polynomial approximation. A series of different applications are possible in areas such as dynamic and robust optimization. © 2014 ACM. |
| ACM SIGEVO |
| http://hdl.handle.net/10993/18764 |
| 10.1145/2576768.2598381 |
| 106779
9781450326629 |
