{'fraction_non_alphanumeric': 0.019202363367799114, 'fraction_numerical': 0.0, 'mean_word_length': 5.277777777777778, '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}}
A Potential of Evolutionary Rule-based Machine Learning for Real World Applications

This paper explores a potential of Evolutionary Rule-based Machine Learning (ERML) by showing how ERML succeeds in real world applications. Generally, ERML is defined as a method that integrates (rule-based) machine learning with evolutionary computation, where the former method contributes to a local search while the latter method contributes to a global search. From such an integrated feature of ERML, one of the fundamental interests in ERML is how to control interactions between learning and evolution to produce a performance that cannot be achieved by either of these methods alone.