<jupyter_start><jupyter_text>Multi-Objective 2-max<jupyter_code>PROBLEM_SIZE = 50
genome = sgx.t.Genome([sgx.allele.Categorical(['0', '1']) for _ in range(PROBLEM_SIZE)])
fitness_function = sgx.fitness.FitnessFunction(lambda i: [i.count('1'), i.count('0')],
                                               best_fitness=[len(genome), len(genome)],
                                               type_=sgx.fitness.Lexicase)
species = sgx.t.Species(genome=genome, fitness_function=fitness_function)
archive = sgx.algorithms.sg(species, max_generation=100)
plt.scatter(*zip(*[e.fitness for e in archive]))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14285714285714285, 'fraction_numerical': 0.015873015873015872, 'mean_word_length': 3.3219178082191783, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '196567', 'license': 'Apache-2.0', 'n_tokens_mistral': 232, 'n_tokens_neox': 211, 'n_words': 28, 'path': 'exp/Sample_2-max.ipynb', 'repo_name': 'squillero/sg-devel', 'size': '630'}