# Infeasibility and structural bias in Differential Evolution

## TL;DR

This study investigates the causes of structural bias in Differential Evolution algorithms, revealing that most configurations are robust against bias except for specific cases, with constraint handling techniques playing a mitigating role.

## Contribution

It extends the understanding of structural bias in DE by examining algorithmic components like constraint handling, and compares its mechanisms to those in GA and PSO.

## Key findings

- Most DE configurations are robust against structural bias.
- DE/current-to-best/1/bin shows clear bias without mitigation.
- Penalty constraint handling reduces bias in biased configurations.

## Abstract

This paper thoroughly investigates a range of popular DE configurations to identify components responsible for the emergence of structural bias - recently identified tendency of the algorithm to prefer some regions of the search space for reasons directly unrelated to the objective function values. Such tendency was already studied in GA and PSO where a connection was established between the strength of structural bias and population sizes and potential weaknesses of these algorithms was highlighted. For DE, this study goes further and extends the range of aspects that can contribute to presence of structural bias by including algorithmic component which is usually overlooked - constraint handling technique. A wide range of DE configurations were subjected to the protocol for testing for bias. Results suggest that triggering mechanism for the bias in DE differs to the one previously found for GA and PSO - no clear dependency on population size exists. Setting of DE parameters is based on a separate study which on its own leads to interesting directions of new research. Overall, DE turned out to be robust against structural bias - only DE/current-to-best/1/bin is clearly biased but this effect is mitigated by the use of penalty constraint handling technique.