{'fraction_non_alphanumeric': 0.015286624203821656, 'fraction_numerical': 0.0025477707006369425, 'mean_word_length': 5.717948717948718, '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}}
Comparative Study on Fitness Landscape Approximation with Fourier Transform

We propose to apply n dimensional discrete Fourier transform (DFT) to a fitness landscape, search an elite individual using obtained principal frequency component and accelerate evolutionary computation (EC) search. a comparative evaluation with our previous works is conducted using eight benchmark functions. the evaluation shows that our proposed approach can obtain the accurate fitness landscape than that with 1 dimensional DFT, and EC acceleration performance can be improved significantly. However, it needs more computational time in the process of conducting n dimensional DFT than that in 1 dimension. We also investigate the computational complexity of the two approaches and some related issues.