{'fraction_non_alphanumeric': 0.026385224274406333, 'fraction_numerical': 0.0, 'mean_word_length': 5.35754189944134, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Reducing bias in multi-objective optimization benchmarking

The performance assessment of multi-objective optimization algorithms involves a user-preference-based selection of a single quality indicator used as a performance measure. A single quality indicator maps the approximation set (i.e., high-dimensional data) into a real value (i.e, one-dimensional data). However, it is well known that the selection of the quality indicator can have a huge impact on the benchmarking conclusions. This invites researchers to present only results for quality indicators that are in favor of the desired algorithm, or performing bias performance assessment. To go beyond this, we proposed a novel ranking scheme that reduces the bias in the user-preference selection by comparing the high-dimensional data of approximations sets and consequently provides more robust statistical results. The selection of a quality indicator is only required in cases when high-dimensional distributions of the approximation sets differ. By performing such analyses, experimental results show that the cases affected by the user-preference selection are reduced.