GENETIC ALGORITHM AND DIFFERENTIAL EVOLUTION ALGORITHM COMPARED ON A NOVEL APPLICATION DOMAIN

Yıl 2016, Cilt: 17 Sayı: 2, 45 - 56, 30.12.2016

Didem Abidin

Öz

In this paper, the performances of genetic algorithms (GA) and differential evolution (DE), which are two of the most popular optimization techniques used, are compared. There exist many other studies which compare these two; however, comparing those on an education material domain will be the contribution to the literature. The problem is stated as a sequencing problem of education material, in which the order of the topics covered really matters. Selection of the contents of the courses to be given to the students is an important factor to improve the level of education of the students. Representing the content of a course in the correct order is a critical task for instructors. In this study, the importance of the order of the contents of a course was emphasized and the performance of a course content sequencing mechanism using GA and DE was compared. The results put forward that, sequencing the course contents with GA performs better; however, DE is also obviously successful with a close score to that of the GA's.

Anahtar Kelimeler

Genetic algorithm, Differential evolution, Sequenzing, Optimization

GENETİK ALGORİTMA VE DİFERANSİYEL EVRİM ALGO-RİTMASININ YENİ BİR UYGULAMA ALANINDA KARŞILAŞTIRILMASI

Öz

Bu çalışmada, genetik algoritmalar (GA) ve diferansiyel evrim (DE) algoritmaları gibi çok popüler iki optimizayson tekniği kullanılarak performans karşılaştırmaları yapılmıştır. Bu iki tekniği kıyaslayan pek çok çalışma yapılmış olsa da, bu kıyaslamanın algoritmaların eğitim materyalleri üzerinde kullanılarak yapılmış olması literatüre katkı değerinde olacaktır. Ele alınan problem bir sıralama problemi olup, işlenen konuların sırası önem kazanmaktadır. Ders içeriğinin doğru seçimi, öğrencilerin eğitim seviyesinin yükselebilmesi için çok önemlidir. Dersin öğretim üyesi için, ders içeriğini doğru sıra ile aktarmak kritik bir görevdir. Bu çalışmada, bir desin içeriğinin doğru sırada aktarılmasının önemine dikkat çekilmiş ve GA ile DE tekniklerinin kullanılmasıyla oluşturulmuş ders içeriği sıralama mekanizmasının performans karşılaştırmaları yapılmıştır. Alınan sonuçlar, GA'nın DE tekniğinden biraz daha iyi sonuçlar elde ettiğini göstermiştir.

Anahtar Kelimeler

Genetik algoritmalar, Differansiyel evrim, Sıralama, Optimizasyon

Kaynakça

• ABIDIN, D., ÇAKIR, Ş. (2011): Rule-Based Genetic Algorithm for In-Service Training Curriculum Plan. Proc International Conference on Computers, Digital Communications and Computing (ICDCCC’11), Barcelona, Spain:160 – 166.
• ABIDIN, D., ÇAKIR, Ş. (2014): Analysis of a Rule Based Curriculum Plan Optimization System with Spearman Rank Correlation. Turkish Journal of Electrical Engineering and Computer Sciences, 22:176-190.
• AGRAWAL, R., SRIKANT, R. (1994): Fast Algorithms for Mining Association Rules. Proc 20th International Conference on Very Large Databases, Morgan Kaufmann, Santiago, Chile:487-499.
• BALLI, S., et al. (2009): Neuro – Fuzzy Decision Support System for Selecting Players in Basketball. Journal of İstanbul Technical University, 8:15-25.
• BISSONETTE, V.L. (2011): Critical Values of the t Distribution. [Online]: http://facultyweb.berry.edu/vbissonnette/tables/t.pdf
• DE MARCOS, L., et al. (2008): Competency-Based Curriculum Sequencing: Comparing Two Evolutionary Approaches. Proc IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, Australia:339 – 342.
• ELSAYED, S.M., SARKER, R.A., ESSAM, D.L. (2012): On an Evolutionary Approach for Constrained Optimization Problem Solving. Applied Soft Computing, Elsevier, 12:3208-3227.
• GARCIA-CAMACHO, F., et al. (2011): Genetic Algorithm Based Medium Optimization for a Toxic Dinoflagellate Microalga. Harmful Algae, 10:697-701.
• GHOSH, S., et al. (2012): A Differential Covariance Matrix Adaptation Evolutionary Algorithm for Real Parameter Optimization. Information Sciences 182:199-219, Elsevier.
• GOLDBERG, D.E. (1989): Genetic Algorithms in Search, Optimization and Machine Learning. Reading, Addison Wesley.
• GREFFENSTETTE, J.J., BAKER, J.E. (1989): How Genetic Algorithms Work: A Critical Look at Implicit Parallelism. Proc 3rd International Conference on Genetic Algorithms, San Mateo, CA, USA, Morgan Kaufmann Publishers:20-27.
• HEGERTY, B., HUNG, C.C., KASPRAK, K. (2009): A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems. 1st Workshop on Intelligent Methods in Search and Optimization - WIMSO.
• HOLLAND, H.J. (1975): Adaptation in Natural and Artificial Systems. Cam-bridge: MIT Press.
• HSIEH, S.-T., SUN, T.-Y., LIU, C.-C., (2009): Potential Offspring Production Strategies: An Improved Genetic Algorithm for Global Numerical Optimization. International Journal of Expert Systems with Applications, 36:11088-11098.
• KAMIYAMA, D., TAMURA, K., YASUDA, K. (2010): Down-hill Simplex Method Based Differential Evolution. Proc SICE Annual Conference, Taipei, Taiwan:1641-1646.
• KESKINTÜRK, T. (2006): Differential Evolution Algorithm. İstanbul Trade University Journal of Science, 9: 85-99.
• MI, M., et al. (2010): An Improved Differential Evolution Algorithm for TSP Problem. International Conference on Intelligent Computation Technology and Automation:544-547.
• PRADO, R.S., et al. (2010): Using Differential Evolution for Combinatorial Optimization: A General Approach. Proc IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey:11-18.
• RANKOVIC, V., et. al. (2014): The Mean-Value at Risk Static Portfolio Optimization Using Genetic Algorithm. Computer Science and Information Systems 11(1):89-109.
• ROEVA, O. (2008): Improvement of Genetic Algorithm Performance for Identification of Cultivation Process Models. Proc 9th WSEAS International Conference on Evolutionary Computing (EC’08), Sofia, Bulgaria:34-39.
• ROM, W.O., SLOTNICK, S.A. (2009): Order Acceptance Using Genetic Al-gorithms. Computers & Operations Research Elsevier Ltd., 36:1758-1767.
• SHESKIN, D.J. (2000): Handbook of Parametric and Non-Parametric Statistical Procedures. 2nd Ed. Chapman & Hall / CRC.
• STORN, R., PRICE, K. (1995): Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley.
• STORN, R., PRICE, K. (1997): Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Journal of Global Optimization 11:341-359.
• WANG, L.-J., HU, D.-W., GONG, R.-Z. (2009): Improved Genetic Algorithm for Aircraft Departure Sequencing Problem. Proc 3th IEEE International Conference on Genetic and Evolutionary Computing:35-38.
• WU, M.-C., SHIH, C.-F., CHEN, C.-F. (2009): An Efficient Approach to Cross - Tab Route Planning for Water Manufacturing. International Journal of Expert Systems with Applications 36:11962-11968.
• YUN, Y., GEN, M., MOON, C. (2010): Hybrid Genetic Algorithm with Adaptive Local Search for Precedence-Constrained Sequencing Problems. Proc 40th IEEE Computers and Industrial Engineering Conference (CIE’10):1-6.
• ZHANG, J., et al. (2008): Differential Optimization for Discrete Optimization: An Experimental Study on Combinatorial Auction Problems. IEEE Congress on Evolutionary Computation (CEC'08), Hong Kong.