TY - GEN
T1 - Decomposition-Based Multi-objective Landscape Features and Automated Algorithm Selection
AU - Cosson, Raphaël
AU - Derbel, Bilel
AU - Liefooghe, Arnaud
AU - Aguirre, Hernán
AU - Tanaka, Kiyoshi
AU - Zhang, Qingfu
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2021
Y1 - 2021
N2 - Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for developing general-purpose automated solvers based on techniques from statistics and machine learning. In this paper, we push a step towards the development of a landscape-aware approach by proposing a set of landscape features for multi-objective combinatorial optimization, by decomposing the original multi-objective problem into a set of single-objective sub-problems. Based on a comprehensive set of bi-objective ρmnk-land-scapes and three variants of the state-of-the-art Moea/D algorithm, we study the association between the proposed features, the global properties of the considered landscapes, and algorithm performance. We also show that decomposition-based features can be integrated into an automated approach for predicting algorithm performance and selecting the most accurate one on blind instances. In particular, our study reveals that such a landscape-aware approach is substantially better than the single best solver computed over the three considered Moea/d variants.
AB - Landscape analysis is of fundamental interest for improving our understanding on the behavior of evolutionary search, and for developing general-purpose automated solvers based on techniques from statistics and machine learning. In this paper, we push a step towards the development of a landscape-aware approach by proposing a set of landscape features for multi-objective combinatorial optimization, by decomposing the original multi-objective problem into a set of single-objective sub-problems. Based on a comprehensive set of bi-objective ρmnk-land-scapes and three variants of the state-of-the-art Moea/D algorithm, we study the association between the proposed features, the global properties of the considered landscapes, and algorithm performance. We also show that decomposition-based features can be integrated into an automated approach for predicting algorithm performance and selecting the most accurate one on blind instances. In particular, our study reveals that such a landscape-aware approach is substantially better than the single best solver computed over the three considered Moea/d variants.
UR - https://www.scopus.com/pages/publications/85107355853
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85107355853&origin=recordpage
U2 - 10.1007/978-3-030-72904-2_3
DO - 10.1007/978-3-030-72904-2_3
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783030729035
T3 - Lecture Notes in Computer Science
SP - 34
EP - 50
BT - Evolutionary Computation in Combinatorial Optimization
A2 - Zarges, Christine
A2 - Verel, Sébastien
PB - Springer
CY - Cham
T2 - 21st European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2021), Held as Part of EvoStar (Evo*) 2021
Y2 - 7 April 2021 through 9 April 2021
ER -