## 3 Intelligent Content Prefix Classification Techniques
### Proposed Intelligent Hybrid Technique  
The previous section has evaluated the performance of the selected AI algorithms and strongly suggest the superiority of reinforcement learning (RL) under machine learning (ML), and the genetic algorithm (GA) under the evolutionary algorithm (EA) group, for solving classification problem, especially in the case of ICN content prefix.  
The application of EA with ML in AI has been one of the growing research fields with rapid development. Studies in this field which attempt to apply the integration of EA and ML techniques have been proven to be beneficial in both convergence speed and solution quality. One of the well-known adaptations of this approach is the learning classifier system (LCS) [22] which has become a powerful tool in a wide range of applications.  
Simulated annealing (SA), originating from the process of cooling metal, includes searching for a final minimum energy structure. After going through several stages, the final structure is achieved where the structure gets the minimum value. Different from SA, the GA, as discussed in the previous Section 2.2, has several concerns related to the premature convergence in its optimization, due to a high reliance on the crossover operation. This may diminish the overall performance by producing a more homogeneous population and searching for the best solution in the mutation stage. Another concern of GA relates to the way to reach the optimal solution after finding a near-optimal solution. To attain the global optimum and resolve the occurrence of local optima, we propose a hybrid GA with the SA technique to realize an intelligent content prefix classification in ICN. We show the pseudo-code in Table 4.  
The reasons behind the choice of the GA and SA hybrid are because they are proved to be efficient and robust in search processes, making them suitable for solving large combinatorial optimization problems. Unlike the other algorithms, GA has a strong global search ability, while SA has strong local search ability and no premature problems. Therefore, the hybridization of GA and SA overcome  
Figure 4: Average standard deviation of different evaluated algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**EA**} & \multicolumn{3}{c}{**SI**} & \multicolumn{3}{c}{**ML**} \\ \hline  & **GA** & **DE** & **BBO** & **PSO** & **ACO** & **ABC** & **MLP** & **RL** \\ \hline Nfe & 11.84 & 21.94 & 19.58 & 12.29 & 14.09 & 34.20 & 8.18 & 4.70 \\ SD & 5.05 & 9.70 & 2.11 & 7.87 & 24.06 & 8.14 & 0.08 & 0.02 \\ CT & 3.88 & 3.01 & 2.72 & 3.19 & 5.75 & 1.24 & 1.32 & 1.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance results of the selected AI algorithms.  
the limits of each of the two methods, bringing into play their respective advantages, and improve the solving efficiency. The combination of SA rules serves as a validation algorithm for the outcome of the GA and to detect unacceptable calculation results.  
Figure 5 summarizes our proposal of intelligent adaptive classification technique which integrates previously-evaluated AI algorithms and the LCS adaptation. There are two main components of the classification techniques which are learning component and discovery component. The learning component features RL, observes the environment, then selects and performs actions [23]. If the action is favorable, it obtains rewards in return. Otherwise, it receives penalties in the form of negative rewards. The discovery component features genetic algorithm-simulated annealing (GA-SA), processes the population evolution by introducing a fitness function, which is proportional to the precise prediction of the reward.  
We then assess the performance of the discovery component using GA and compare with our hybrid GA-SA approach. The performance result of the proposed hybrid technique is depicted in Figure 6. Based on this result, we conclude that SA rules shape the integration into the new population phase of GA to