{'fraction_non_alphanumeric': 0.014588859416445624, 'fraction_numerical': 0.0, 'mean_word_length': 5.088709677419355, '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}}
An Evolutionary Approach to the School Timetabling Problem

We present a general heuristic technique that solves a timetabling problem, such as school timetabling. It is based on a Hopfield-type neural network, whose complexity was significantly reduced by incorporating a genetic algorithm into a very first stage of the timetabling process. The goal of the GA was to initialize the network so that the number of neurons and their connections decreased, and as a consequence the computation speeded up. By using this technique, we were able to produce school timetables subject to both hard and soft constraints in a reasonable polynomial time. The technique proved to be an efficient method for solving a NP-complete combinatorial optimization problem.