Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization


  • Tirana Noor Fatyanosa Universitas Brawijaya http://orcid.org/0000-0002-2801-5947
  • Andreas Nugroho Sihananto Universitas Brawijaya
  • Gusti Ahmad Fanshuri Alfarisy Faculty of Computer Science, Universitas Brawijaya
  • M Shochibul Burhan Universitas Brawijaya
  • Wayan Firdaus Mahmudy Universitas Brawijaya




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

Author Biographies

Tirana Noor Fatyanosa, Universitas Brawijaya

Faculty of Computer Science

Wayan Firdaus Mahmudy, Universitas Brawijaya

Faculty of Computer Science


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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. https://doi.org/10.25126/jitecs.20161215