**5. Conclusion**

This work presented a Cultural Algorithm (CA) with single population (CA-S) and multi population (CA-IM) in order to improve the search performance on MKP. It was observed that CA-S improves the convergence reliability and search speed. However, CA-S is not enough to reach global optimum for most problems presented. Our cultural algorithm implementation with island model (CA-IM\_1 and CA-IM\_2) allows the migration among islands sub-populations and main population through belief space structures that represent the cultural knowledge available in Cultural Algorithms.

The results have shown that the CA-IM\_1 is better than CA-IM\_2 for the benchmarks selected. The results have also shown that the CA-IM\_1 and CA-IM\_2 perform the optimum search and reach optimum values equally or above the ones reached by algorithms DGA and DGA-SRM that were chosen for comparison. The positive results obtained, give support the idea that this is a desirable approach for tackling highly constrained NP-complete problems such as the MKP. In addition, it is possible that the hybridization of cultural algorithms based on population of GA with local search techniques improves the results obtained by standard CAs. In a future work, a study will be done about the behavior of the sub-populations that are eliminated and recreated randomly. In addition a local search will be implemented to CAs as much for standard CA (single population) as for CA-IM (multi population) so as to verify improvements on these algorithms.
