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.

翻译：评估启发式优化算法的性能对于确定其在各种条件下的表现至关重要。近期，BIAS工具箱被引入作为一种行为基准，用于检测搜索算法中的结构偏差。该工具箱可用于识别现有算法中的偏差，也可用于测试新开发算法中是否存在偏差。本文提出了一种名为Deep-BIAS的新型、可解释的深度学习扩展方法，对BIAS工具箱进行了拓展。原始工具箱使用39项统计检验和随机森林模型来预测结构偏差的存在性和类型，而Deep-BIAS方法则利用经过训练的深度学习模型，基于原始性能分布直接检测结构偏差的强度与类型。通过一系列包含多种结构偏差场景的实验，我们验证了Deep-BIAS的有效性。此外，我们展示了将该工具箱应用于336种最先进优化算法的结果，结果表明这些算法中存在各类结构偏差，尤其是偏向目标空间中心或呈现离散化行为。Deep-BIAS方法在偏差检测与结构偏差类型分类两方面均优于BIAS工具箱。此外，还可利用可解释人工智能技术推导出相应的解释。