MO-ELA: Rigorously Expanding Exploratory Landscape Features for Automated Algorithm Selection in Continuous Multi-Objective Optimisation

2026-01-24

AI Summary

This work addresses the lack of effective exploratory landscape features in existing automated algorithm selection methods for multi-objective optimization. It systematically introduces, for the first time, five complementary feature categories that comprehensively characterize the difficulty of continuous multi-objective optimization problems. These features are derived through random sampling in both decision and objective spaces, combined with non-dominated sorting, descriptive statistics, principal component analysis, graph-based modeling, and gradient estimation. Evaluated on standard benchmark suites, the proposed features significantly enhance algorithm selection performance, consistently ranking among the most important predictors and enabling selector models to approach the performance of the virtual best solver. This contribution provides a novel and effective toolkit for automated algorithm configuration in multi-objective optimization.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Dimensionality Reduction/Feature Selection
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Automated Algorithm Selection (AAS) is a popular meta-algorithmic approach and has demonstrated to work well for single-objective optimisation in combination with exploratory landscape features (ELA), i.e., (numerical) descriptive features derived from sampling the black-box (continuous) optimisation problem. In contrast to the abundance of features that describe single-objective optimisation problems, only a few features have been proposed for multi-objective optimisation so far. Building upon recent work on exploratory landscape features for box-constrained continuous multi-objective optimization problems, we propose a novel and complementary set of additional features (MO-ELA). These features are based on a random sample of points considering both the decision and objective space. The features are divided into 5 feature groups depending on how they are being calculated: non-dominated-sorting, descriptive statistics, principal component analysis, graph structures and gradient information. An AAS study conducted on well-established multi-objective benchmarks demonstrates that the proposed features contribute to successfully distinguishing between algorithm performance and thus adequately capture problem hardness resulting in models that come very close to the virtual best solver. After feature selection, the newly proposed features are frequently among the top contributors, underscoring their value in algorithm selection and problem characterisation.

Problem

• multi-objective optimization
• exploratory landscape features
• automated algorithm selection
• problem characterization
• continuous optimization

Innovation

• Exploratory Landscape Analysis
• Multi-objective Optimization
• Automated Algorithm Selection
• MO-ELA
• Problem Characterization