# Structural bias in population-based algorithms

arXiv: https://arxiv.org/abs/1408.5350 · HTML: https://mlsanity.com/paper/1408.5350/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2014.

## Built on by (most cited first, 9 of 9)

- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [Deep-BIAS: Detecting Structural Bias using Explainable AI](https://mlsanity.com/paper/2304.01869/) (2023)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond](https://mlsanity.com/paper/2203.03512/) (2022)
- [A Framework for Knowledge Integrated Evolutionary Algorithms](https://mlsanity.com/paper/2103.16897/) (2021)
- [Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge](https://mlsanity.com/paper/2105.10541/) (2021)
- [A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories](https://mlsanity.com/paper/2404.17323/) (2024)
- [Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization](https://mlsanity.com/paper/2604.20365/) (2026)

## Abstract (arXiv)

Challenging optimisation problems are abundant in all areas of science. Since the 1950s, scientists have developed ever-diversifying families of black box optimisation algorithms designed to address any optimisation problem, requiring only that quality of a candidate solution is calculated via a fitness function specific to the problem. For such algorithms to be successful, at least three properties are required: an effective informed sampling strategy, that guides generation of new candidates on the basis of fitnesses and locations of previously visited candidates; mechanisms to ensure efficiency, so that same candidates are not repeatedly visited; absence of structural bias, which, if present, would predispose the algorithm towards limiting its search to some regions of solution space. The first two of these properties have been extensively investigated, however the third is little understood. In this article we provide theoretical and empirical analyses that contribute to the understanding of structural bias. We prove a theorem concerning dynamics of population variance in the case of real-valued search spaces. This reveals how structural bias can manifest as non-uniform clustering of population over time. Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty. These predictions reveal two previously unrecognised aspects of structural bias. Respectively, increasing population size, though ostensibly promoting diversity, will magnify any inherent structural bias, and effects of structural bias are more apparent when faced with difficult problems. Our theoretical result also suggests that two commonly used approaches to enhancing exploration, increasing population size and increasing disruptiveness of search operators, have quite distinct implications in terms of structural bias.