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The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Article Details

Author Biographies

Tirana Noor Fatyanosa, Universitas Brawijaya

Faculty of Computer Science

Wayan Firdaus Mahmudy, Universitas Brawijaya

Faculty of Computer Science
How to Cite
Fatyanosa, T. N., Sihananto, A. N., Alfarisy, G. A. F., Burhan, M. S., & Mahmudy, W. F. (2017). Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization. Journal of Information Technology and Computer Science, 1(2), 82–97.


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