Structural bias in multi-objective optimisation

2026-02-06

AI Summary

This work addresses the underexplored issue of structural bias in multi-objective optimization algorithms, which often arises from algorithmic design and adversely affects the distribution and diversity of solution sets. For the first time, the concept of structural bias is formally extended to the domain of multi-objective optimization. The authors propose a suite of synthetic test problems featuring uninformative objective functions and controllable Pareto fronts, effectively decoupling algorithmic behavior from problem structure. The resulting benchmark framework encompasses diverse front geometries, solution densities, and noise levels, establishing a novel paradigm for detecting algorithmic bias and evaluating robustness in multi-objective optimization.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Search and Optimization: Combinatorial Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Structural bias (SB) refers to systematic preferences of an optimisation algorithm for particular regions of the search space that arise independently of the objective function. While SB has been studied extensively in single-objective optimisation, its role in multi-objective optimisation remains largely unexplored. This is problematic, as dominance relations, diversity preservation and Pareto-based selection mechanisms may introduce or amplify structural effects. In this paper, we extend the concept of structural bias to the multi-objective setting and propose a methodology to study it in isolation from fitness-driven guidance. We introduce a suite of synthetic multi-objective test problems with analytically controlled Pareto fronts and deliberately uninformative objective values. These problems are designed to decouple algorithmic behaviour from problem structure, allowing bias induced purely by algorithmic operators and design choices to be observed. The test suite covers a range of Pareto front shapes, densities and noise levels, enabling systematic analysis of different manifestations of structural bias. We discuss methodological challenges specific to the multi-objective case and outline how existing SB detection approaches can be adapted. This work provides a first step towards behaviour-based benchmarking of multi-objective optimisers, complementing performance-based evaluation and informing more robust algorithm design.

Problem

• structural bias
• multi-objective optimisation
• search space preference
• Pareto-based selection
• algorithmic bias

Innovation

• structural bias
• multi-objective optimisation
• synthetic test problems
• Pareto front
• algorithmic bias