{'fraction_non_alphanumeric': 0.015945330296127564, 'fraction_numerical': 0.0, 'mean_word_length': 5.659090909090909, '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}}
Robust Optimal Design Under Standard Crowding Differential Evolution Framework

In practical optimization problems, it is often desired that a solution is not only of high performance, but also of high robustness, for example, robust to fabrication tolerances. The robust optimal design problem is studied under a standard crowding differential evolution framework in this paper, where the robustness of a solution is indicated by the objective in the worst case of this solution. To evaluate the robust objective function, a simple evolutionary strategy is used, other than the traditional Monte-Carlo simulation. Numerical results indicate that the proposed method can provide faster convergence rate and more accurate solution than Monte-Carlo simulation. This method is then applied to the practical holographic grating design, and achieves satisfactory optimization results