pruvost_evocop2020.bib

@InProceedings{10.1007/978-3-030-43680-3_9,
author="Pruvost, Geoffrey
and Derbel, Bilel
and Liefooghe, Arnaud
and Li, Ke
and Zhang, Qingfu",
editor="Paquete, Lu{\'i}s
and Zarges, Christine",
title="On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D",
booktitle="Evolutionary Computation in Combinatorial Optimization",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="131--147",
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.",
isbn="978-3-030-43680-3"
}