Application of Genetic Algorithm in Common Optimization Problems

Nika Topuria; Omar Kikvidze

doi:10.21467/ias.8.1.17-21

Authors

Nika Topuria Department of Technical Engineering, Akaki Tsereteli State University, Georgia
Omar Kikvidze Department of Technical Engineering, Akaki Tsereteli State University, Georgia

DOI:

https://doi.org/10.21467/ias.8.1.17-21

Abstract

Use of non-deterministic algorithms for solving multi-variable optimization problems is widely used nowadays. Genetic Algorithm belongs to a group of stochastic biomimicry algorithms, it allows us to achieve optimal or near-optimal results in large optimization problems in exceptionally short time (compared to standard optimization methods). Major advantage of Genetic Algorithm is the ability to fuse genes, to mutate and do selection based on fitness parameter. These methods protect us from being trapped in local optima (Most of deterministic algorithms are prone to getting stuck on local optima). In this paper we experimentally show the upper hand of Genetic Algorithms compared to other traditional optimization methods by solving complex optimization problem.

Keywords:

Oncology, Optimization, Genetic, Non-Deterministic, Algorithm

Downloads

Download data is not yet available.

References

C. Johansson, G. Evertsson, “Optimizing Genetic Algorithms for Time Critical Problems”, Master Thesis, Dep. of Software Engineering and Computer Science, Blekinge Institute of Technology, Sweden, June 2003.

D. Djajaputra, et al. “Algorithm and performance of a clinical IMRT beam-angle optimization system.” Physics in medicine and biology, Vol. 48, Issue 19, pp. 3191-212, 2003.

L. Shao, “A survey of beam intensity optimization in IMRT” in Proceedings of the 40th Annual Conference of the Operational Research Society of New Zealand, Department of Engineering Science, University of Auckland, pp. 2-13.

M. Ehrgot, A. Holder, J. Reese. “Beam selection in radiotherapy design.”, Linear Algebra and its Applications, Vol. 428, Issues 5-6, pp. 1272-1312, march, 2008.

H. Rocha, J.M. Dias, B.C. Ferreira, M.C. Lopes, “Beam angle optimization for intensity-modulated radiation therapy using a guided pattern search method.” Physics in medicine and biology, Vol 58-9, pp.2939-2953, 2013.

H. Yarmand, D. Craft, “Effective Heuristic Cuts for Beam Angle Optimization in Radiation Therapy”, Medical Physics, Vol. 40, Issue 6, pp. 387-387, June 2013.

X. Wang, M. Damodaran, “Comparison of Deterministic and Stochastic Optimization Algorithms for Genetic Wing Design Problems”, Aerospace Research Central, Vol. 37, Issue 5, pp. 929-932, May 2012.

A.M. Connor, K. Shea, “A Comparison of Semi-deterministic and Stochastic Search Techniques”, Book: Evolutionary Design and Manufacture, Springer, London, pp. 287-298, 2000.

A. Blake, “Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction.” IEEE Trans. Pattern Anal. Mach. Intell. Vol. 11, Issue 1, pp. 2-12, January 1989.

A. Beloglazov, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers”, Concurrency and Computation Practice and Experience, Vol. 24, Issue 13, pp. 1397-1420, September, 2012.

N.M. Razali, J. Geraghty, “Genetic Algorithm Performance with Different Selection Strategies in Solving TSP”, Conference: International Conference of Computational Intelligence and Intelligent Systems, January, 2011.

M. Mitchell, “Genetic algorithms: An overview”, Complexity, Vol, 1, Issue 1, pp. 31-39, May, 2013.

J. A. Purdy, “Three-Dimensional treatment planning and conformal dose delivery” in Advances in Radiation Therapy, B.B. Mittal, J.A. Purdy, K.K. Ang, Springer US, Vol 93, pp. 19-21.

H, Wieser, E. Cisternas, N, Wahl, S. Ulrich, A. Stadler, H. Mescher, L. Müller, T. Klinge, H. Gabrys, L. Burigo, A. Mairani, S. Ecker, B. Ackermann, M. Ellerbrock, K. Parodi, O. Jäkel, and M. Bangert, “Development of the open‐source dose calculation and optimization toolkit matRad” Meical. Physics, Vol. 44, Issue 6, pp. 2556-2568. June, 2017.

N. Hou, F. He, Y. Zhou, Y. Chen, X. Yan "A Parallel Genetic Algorithm With Dispersion Correction for HW/SW Partitioning on Multi-Core CPU and Many-Core GPU," in IEEE Access, Vol. 6, pp. 883-898, 2018.