{'fraction_non_alphanumeric': 0.012008733624454149, 'fraction_numerical': 0.0, 'mean_word_length': 5.238095238095238, '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}}
Chaotic Clonal Genetic Algorithm for Routing Optimization

Clonal operator which can reserve the elites is introduced in the selection step of traditional genetic algorithm (GA) to accelerate the local convergence speed. Chaotic search which is randomness and ergodicity is applied in crossover and mutation operators to avoid the algorithm stopping at a local extreme value. The above hybrid GA is called chaotic clonal GA (CCGA) which can overcome the instability of optimizing processes and results in traditional GA by the certainty of chaotic trajectory. The CCGA is applied to solve the problem of load balance routing in differentiated service networks. The routing optimization model is created and the optimizing objective is load balance and small path length. The simulation results show that CCGA has fast convergence speed and high stability. It can meet the requirements of important business routings.