{'fraction_non_alphanumeric': 0.012965964343598054, 'fraction_numerical': 0.0, 'mean_word_length': 5.717391304347826, '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}}
Genetic construction of neural logic networks

Genetic construction of neural networks pertains to a process where the architecture of a neural network evolves through genetic algorithms or genetic programming. This paper describes a genetic programming approach that constructs neural logic networks from training examples. Neural logic network, unlike ordinary networks, allows representation of a great multitude of logic operations used in human decision-making. The proposed method not only enables construction of the network from scratch, but also preserves the semantic contents of the network after training.