  Evaluating the performance of heuristic optimisation algorithms is essential
to determine how well they perform under various conditions. Recently, the BIAS
toolbox was introduced as a behaviour benchmark to detect structural bias (SB)
in search algorithms. The toolbox can be used to identify biases in existing
algorithms, as well as to test for bias in newly developed algorithms. In this
article, we introduce a novel and explainable deep-learning expansion of the
BIAS toolbox, called Deep-BIAS. Where the original toolbox uses 39 statistical
tests and a Random Forest model to predict the existence and type of SB, the
Deep-BIAS method uses a trained deep-learning model to immediately detect the
strength and type of SB based on the raw performance distributions. Through a
series of experiments with a variety of structurally biased scenarios, we
demonstrate the effectiveness of Deep-BIAS. We also present the results of
using the toolbox on 336 state-of-the-art optimisation algorithms, which showed
the presence of various types of structural bias, particularly towards the
centre of the objective space or exhibiting discretisation behaviour. The
Deep-BIAS method outperforms the BIAS toolbox both in detecting bias and for
classifying the type of SB. Furthermore, explanations can be derived using XAI
techniques.
