@inproceedings{8eee5e95e7ac4c60a5c7dc9455641a3f,
title = "On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D",
abstract = "This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established Moea/d framework to support the smooth integration of different strategies for sub-problem selection, while emphasizing the role of the population size and of the number of offspring created at each generation. By conducting a comprehensive empirical analysis on a wide range of multi- and many-objective combinatorial NK landscapes, we provide new insights into the combined effect of those parameters on the anytime performance of the underlying search process. In particular, we show that even a simple random strategy selecting sub-problems at random outperforms existing sophisticated strategies. We also study the sensitivity of such strategies with respect to the ruggedness and the objective space dimension of the target problem.",
author = "Geoffrey Pruvost and Bilel Derbel and Arnaud Liefooghe and Ke Li and Qingfu Zhang",
note = "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).; 20th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2020, held as part of Evostar 2020 ; Conference date: 15-04-2020 Through 17-04-2020",
year = "2020",
month = apr,
doi = "10.1007/978-3-030-43680-3_9",
language = "English",
isbn = "978-3-030-43679-7",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Cham",
pages = "131--147",
editor = "Lu{\'i}s Paquete and Christine Zarges",
booktitle = "Evolutionary Computation in Combinatorial Optimization",
}