# Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance

arXiv: https://arxiv.org/abs/2507.01638 · HTML: https://mlsanity.com/paper/2507.01638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [On the Tractability of SHAP Explanations](https://mlsanity.com/paper/2009.08634/) (2020)
- [Comparing Algorithm Selection Approaches on Black-Box Optimization Problems](https://mlsanity.com/paper/2306.17585/) (2023)
- [One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes](https://mlsanity.com/paper/2006.11547/) (2020)

## Abstract (arXiv)

We present an analysis of landscape features for predicting the performance of multi-objective combinatorial optimization algorithms. We consider features from the recently proposed compressed Pareto Local Optimal Solutions Networks (C-PLOS-net) model of combinatorial landscapes. The benchmark instances are a set of rmnk-landscapes with 2 and 3 objectives and various levels of ruggedness and objective correlation. We consider the performance of three algorithms -- Pareto Local Search (PLS), Global Simple EMO Optimizer (GSEMO), and Non-dominated Sorting Genetic Algorithm (NSGA-II) - using the resolution and hypervolume metrics. Our tailored analysis reveals feature combinations that influence algorithm performance specific to certain landscapes. This study provides deeper insights into feature importance, tailored to specific rmnk-landscapes and algorithms.