{'fraction_non_alphanumeric': 0.02664796633941094, 'fraction_numerical': 0.009817671809256662, 'mean_word_length': 5.0, '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 genetic algorithm applied to a classic job-shop scheduling problem

Job-shop scheduling is essentially an ordering problem. A new encoding scheme for a classic job-shop scheduling problem is presented, by which a schedule directly corresponds to an ordering string. For the new encoding, a simple but highly effective crossover operation is contrived, and the problem of infeasibility in genetic generation is naturally overcome. Within the framework of the newly designed genetic algorithm, the NP-hard classic job-shop scheduling problem can be efficiently solved with high quality. Moreover the optimal solutions of the two famous benchmarks, the Fisher and Thompson's 10 × 10 and 20 × 5 problems, are found.