# On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D

arXiv: https://arxiv.org/abs/2004.06961 · HTML: https://mlsanity.com/paper/2004.06961/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2401.02160/) (2024)
- [Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition](https://mlsanity.com/paper/2112.11939/) (2021)
- [Automated Few-Shot Time Series Forecasting based on Bi-level Programming](https://mlsanity.com/paper/2203.03328/) (2022)
- [Art-Attack: Black-Box Adversarial Attack via Evolutionary Art](https://mlsanity.com/paper/2203.04405/) (2022)

## Abstract (arXiv)

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.