**7. Conclusions**

Since HPT is the most challenging aspect of ANN studies, it is mostly obtained by trial-and-error, affecting its performance. This article proposed a new approach based on cultural evolution and parallel computing to achieve a near-optimal structure of SAAE in a reasonable time for automatic personality perception. We used the concept of parallelism and information on different regions of the search space to improve the search spaces in MIC and exchanged them between islands to provide greater population diversity. The proposed approach was implemented on three complex benchmarks, and six criteria evaluated our method's performance in comparison with four basic optimization methods. The results showed that our approach outperforms other traditional optimization and newly published algorithms in four aspects: (1) convergence speed, (2) precision, (3) escaping from entrapment in local optima, and (4) repeatability. As an indication of our method's performance, we increased the problem complexity by increasing the number of variables up to 30. The outcomes demonstrated the reliability of the MIC method, especially LM and EM. Subsequently, five hyper-parameters of SAAE were optimized. Since the tuning of hyper-parameters affects over-fitting and under-fitting, we introduced a new cost function to control them during the optimization process.

In comparison with the results of our previous published work, the outcomes of applying MIC to SAAE indicated 3.3% (3.1%) for consciousness, 5.1% (7.5%) for agreeableness, 5.9% (14.3%) for openness, 5.6% (10.1%) for extraversion, and 12.7% (3.6%) for neuroticism.

**Author Contributions:** Writing—review & editing, E.J.Z., M.T., A.K., C.H., M.V., N.N. and T.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
