Abstract: A noticeable objective of this work is to experiment and test an optimization problem through comparing hill-climbing method with a hybrid method combining hill-climbing and Latin-hyper-cube. These two methods are going to be tested operating the same data-set in order to get the comparison result for both methods. The result shows that the hybrid model has a better performance than hill-climbing. Based on the number of global optimum value occurrence, the hybrid model outperformed 7.6% better than hill-climbing, and produced more stable average global optimum value. However, the model has a little longer running time due to a genuine characteristic of the model itself.

Calista Elysia, Michelle Hartanto and Ditdit Nugeraha Utama. “Hybrid Latin-Hyper-Cube-Hill-Climbing Method for Optimizing: Experimental Testing”. International Journal of Advanced Computer Science and Applications (IJACSA) 10.9 (2019). http://dx.doi.org/10.14569/IJACSA.2019.0100955

@article{Elysia2019,
title = {Hybrid Latin-Hyper-Cube-Hill-Climbing Method for Optimizing: Experimental Testing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100955},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100955},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {9},
author = {Calista Elysia and Michelle Hartanto and Ditdit Nugeraha Utama}
}