{'fraction_non_alphanumeric': 0.009501187648456057, 'fraction_numerical': 0.0, 'mean_word_length': 5.108695652173913, '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}}
Evolving Neural Network Ensembles by Fitness Sharing

The difference between evolving neural networks and evolving neural network ensembles is that the solution of evolving neural networks is an evolved neural network while the solution of evolving neural network ensemble is an evolved population of neural networks. In the practice of evolving neural network ensemble, it is common that each individual rather the whole population is evaluated. During the evolution, the solution of evolving neural networks would be better and better while it might not be the case for the solution of evolving neural network ensembles. It suggests that the final evolved population might be worse so that it is not wise to choose the final population as a solution. Through experimental studies, this paper gives ideas of how to evolve better populations.