{'fraction_non_alphanumeric': 0.02127659574468085, 'fraction_numerical': 0.0, 'mean_word_length': 5.776, '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}}
Construction of Lyapunov Functions using Multi-Objective Genetic Algorithm

In nonlinear dynamical systems such as complex and high-order systems, it is difficult to analytically compute the Lyapunov function considering the nonlinear dynamics. Hence, in such cases, numerical methods offer viable alternatives. This paper presents a new approach to construct appropriate Lyapunov functions based on evolutionary theory and biological mathematics, using the multi-objective genetic algorithm (MO-GA), which is a meta-heuristic algorithm for nonlinear dynamical systems. The use of genetic algorithm allows for direct generation of initial data from the Lyapunov theory, leading to the selection and construction of suitable candidates in the shortest time possible. The proposed method has been implemented and validated through several examples.