Structural bias in multi-objective optimisation

TL;DR

This paper investigates the concept of structural bias in multi-objective optimisation, introducing a methodology and test suite to analyze algorithmic preferences independently of problem structure, aiding in the development of more robust algorithms.

Contribution

It extends the concept of structural bias to multi-objective optimisation and proposes a synthetic test suite to study bias independently of fitness guidance.

Findings

Introduces a suite of synthetic multi-objective test problems with controlled Pareto fronts.

Provides a methodology to detect and analyze structural bias in multi-objective algorithms.

Lays groundwork for behaviour-based benchmarking of multi-objective optimisers.

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.