Conference Scheduling with Epigenetic Algorithm

Yıl 2024, Cilt: 27 Sayı: 1, 139 - 152, 29.02.2024

Ercan Atagün , Serdar Biroğul

https://doi.org/10.2339/politeknik.1010504

Öz

The most important of the activities where the presentations of scientific studies take place are academic conferences. The days, halls, and sessions are determined in advance to organize multidisciplinary conferences and this process is called conference scheduling. In multidisciplinary conferences, in the scheduling of presentations, the coexistence of studies belonging to the same fields in the same sessions is very important for the conference listener and the conference speaker. In this context, the multidisciplinary conference scheduling problem is considered a multi-constraint optimization problem. Multi-constraint optimization problems are solved with heuristic optimization techniques, not traditional optimization methods. In this study, the problem of conference scheduling is addressed using multidisciplinary conference data. The solution to the conference scheduling problem was realized with Genetic Algorithm (GA) and Epigenetic Algorithm (EGA) using C# programming language. In the study, experimental results obtained with GA and EGA were examined. As a result of this examination, it was seen that EGA achieved better results in fewer iterations compared to classical GA.  

Anahtar Kelimeler

Genetic algorithm, epigenetic algorithm, conference scheduling.

Epigenetik Algoritma ile Konferans Çizelgeleme

Öz

Bilimsel çalışmaların sunumlarının yapıldığı etkinliklerin en önemlileri akademik konferanslardır. Multidisipliner konferanslar düzenlemek için günler, salonlar ve oturumlar önceden belirlenir ve bu sürece konferans çizelgeleme denir. Multidisipliner konferanslarda, sunumların planlanmasında aynı alanlara ait çalışmaların aynı oturumlarda bir arada bulunması konferans dinleyicisi ve konferans konuşmacısı için çok önemlidir. Bu bağlamda multidisipliner konferans planlama problemi çok kısıtlı bir optimizasyon problemi olarak görülmektedir. Çok kısıtlı optimizasyon problemleri, geleneksel optimizasyon yöntemleriyle değil, sezgisel optimizasyon teknikleriyle çözülür. Bu çalışmada, multidisipliner konferans verisi kullanılarak konferans planlaması sorunu ele alınmaktadır. Konferans çizelgeleme probleminin çözümü Genetik Algoritma (GA) ve Epigenetik Algoritma (EGA) ile C# programlama dili kullanılarak gerçekleştirilmiştir. Çalışmada GA ve EGA ile elde edilen deneysel sonuçlar incelenmiştir. Bu inceleme sonucunda EGA'nın klasik GA'ya göre daha az iterasyonda daha iyi sonuçlar elde ettiği görülmüştür.

Anahtar Kelimeler

Genetik algoritma, epigenetik algoritma, konferans çizelgeleme

Kaynakça

• [1] Zilberstein, S., Koehler, J., and Koenig, S. , “The fourteenth international conference on automated planning and scheduling (ICAPS-04)”, AI Magazine, 25(4), 101-101, (2004).
• [2] Aktay, S., “How to organize a symposium: tracking digital footprints”, Electronic Turkish Studies, 12(23), (2017).
• [3] Andlauer, O., Obradors-Tarragó, C., Holt, C., and Moussaoui, D. , “How to organize and manage scientific meetings”, Psychiatry in Practice: Education, Experience, and Expertise, 10(50), 97, (2016).
• [4] Nicholls, M. G. , “A small-to-medium-sized conference scheduling heuristic incorporating presenter and limited attendee preferences”, Journal of the Operational Research Society, 58(3), 301-308, (2007).
• [5] Potthoff, R. F., and Brams, S. J. , “Scheduling of panels by integer programming: results for the 2005 and 2006 new orleans meetings”, Public Choice, 131(3), 465-468, (2007).
• [6] Zulkipli, F., Ibrahim, H., and Benjamin, A. M., “Optimization capacity planning problem on conference scheduling”, In 2013 IEEE Business Engineering and Industrial Applications Colloquium (BEIAC), (pp. 911-915). (2013).
• [7] Eglese, R. W., and Rand, G. K. , “Conference seminar timetabling”, Journal of the Operational Research Society, 38(7), 591-598, (1987).
• [8] Ibrahim, H., Ramli, R., and Hassan, M. H., “Combinatorial design for a conference: constructing a balanced three-parallel session schedule”, Journal of Discrete Mathematical Sciences and Cryptography, 11(3), 305-317, (2008).
• [9] Sampson, S. E., and Weiss, E. N. , “Designing conferences to improve resource utilization and participant satisfaction”, Journal of the Operational Research Society, 47(2), 297-314, (1996).
• [10] Sampson, S. E. , “Practical implications of preference‐based conference scheduling”, Production and Operations Management, 13(3), 205-215, (2004).
• [11] Thompson, G. M. , “Improving conferences through session scheduling”, Cornell Hotel and Restaurant Administration Quarterly, 43(3), 71-76, (2002).
• [12] Bhardwaj, A., Kim, J., Dow, S., Karger, D., Madden, S., Miller, R., and Zhang, H. , ”Attendee-sourcing: Exploring the design space of community-informed conference scheduling”, In Second AAAI conference on human computation and crowdsourcing, Pittsburgh, USA, pp. 2-10, (2014).
• [13] Tanaka, M., Mori, Y., and Bargiela, A. (2002) “Granulation of keywords into sessions for timetabling conferences”, in Proceedings of soft computing and intelligent systems (SCIS), Tsukuba, Japan, pp.1-5
• [14] Mori, Y., Tanaka, M. , “A hybrid genetic algorithm for timetabling of conference programs”, in: Proceedings of the 4th International Conference on the Practice and Theory of Automated Timetabling (PATAT 2002), Gent, Belgium, pp.421–440, (2002).
• [15] Edis, E. & Sancar Edis, R. , “An integer programming model for the conference timetabling problem’, Celal Bayar University Journal of Science, 9(2), 55-62, (2013).
• [16] Stidsen, T., Pisinger, D., and Vigo, D. , “Scheduling EURO-k conferences”, European Journal of Operational Research, 270(3), 1138-1147, (2018).
• [17] Correia, R., Subramanian, A., Bulhões, T., and Penna, P. H. V. , “Scheduling the Brazilian or conference”, Journal of the Operational Research Society, 1-12, (2021).
• [18] Bulhões, T., Correia, R., and Subramanian, A. , “Conference scheduling: A clustering-based approach”, European Journal of Operational Research, 297(1), 15-26, (2022).
• [19] Doshi, V., Tuteja, S., Bharadwaj, K., Tantillo, D., Marrinan, T., Patton, J., and Marai, G. E. , “StickySchedule: an interactive multi-user application for conference scheduling on large-scale shared displays”, In Proceedings of the 6th ACM International Symposium on Pervasive Displays,(pp. 1-7), (2017).
• [20] Castaño, F., Velasco, N., and Carvajal, J. , “Content-based conference scheduling optimization”, IEEE Latin America Transactions, 17(04), 597-606, (2019).
• [21] Eltayeb, I. S., and Ahmed, A. S. , “A comparison of selection hyper-heuristic approaches on the conference scheduling optimization problem”, In 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), (pp. 1-6), IEEE, (2020).
• [22] Howells, J., Ramlogan, R., and Cheng, S. L. , ”Universities in an open innovation system: a UK perspective”, International Journal of Entrepreneurial Behavior & Research, 18(4), 440–456, (2012).
• [23] Dimitrios, N. K., Sakas, D. P., and Vlachos, D. S. , “Modeling the scientific dimension of academic conferences”, Procedia-social and behavioral sciences, 147, 576-585, (2014).
• [24] Holland, J.H. , “Adaptation in natural and artificial systems”, The University of Michigan Press, Ann Arbor, USA, (1975).
• [25] Goldberg, D. E. , “Genetic algorithms in search, optimization, and machine learning”, Addion wesley, Boston, USA, (1989).
• [26] Davis, L. , “Handbook of genetic algorithms”, Van Nostrand Reinhold, Michigan, USA, (1991).
• [27] Hilali-Jaghdam, I., Ishak, A. B., Abdel-Khalek, S., and Jamal, A. , “Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study”, Computer Communications, 162, 83-93, (2020).
• [28] Zhang, B., Wang, X., and Wang, H. , “Virtual machine placement strategy using cluster-based genetic algorithm”, Neurocomputing, 428, 310-316, (2021).
• [29] Higazy, M., and Alyami, M. A. , “New caputo-fabrizio fractional order SEIASqEqHR model for COVID-19 epidemic transmission with genetic algorithm based control strategy”, Alexandria Engineering Journal, 59(6), 4719-4736, (2020).
• [30] Yüksek, G., Mete, A. N., and Alkaya, A. , “PID parametrelerinin lqr ve ga tabanlı optimizasyonu: sıvı seviye kontrol uygulaması”, Politeknik Dergisi, 23(4), 1111-1119, (2020).
• [31] Karasu, S., and Saraç, Z. “Güç kalitesi bozulmalarının hilbert-huang dönüşümü, genetik algoritma ve yapay zeka/makine öğrenmesi yöntemleri ile sınıflandırılması”, Politeknik Dergisi, 23(4), 1219-1229, (2020).
• [32] Hartl, D. L., and Jones, E. W. , “Genetics: analysis of genes and genomes”, Jones & Bartlett Learning, Canada, (2009).
• [33] Jaenisch, R., and Bird, A. , “Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals”, Nature genetics, 33(3), 245-254, (2003).
• [34] Madlung, A., and Comai, L. , “The effect of stress on genome regulation and structure”, Annals of Botany, 94(4), 481-495, (2004).
• [35] Jirtle, R. L. , “Epigenome: the program for human health and disease”, Epigenomics, 1(1), 13-16, (2009).
• [36] Siegmund, K. D., Connor, C. M., Campan, M., Long, T. I., Weisenberger, D. J., Biniszkiewicz, D., ... and Akbarian, S. , “DNA methylation in the human cerebral cortex is dynamically regulated throughout the life span and involves differentiated neurons”, PloS one, 2(9), e895, (2007).
• [37] Kouzarides, T., “Chromatin modifications and their function”. Cell, 128(4), 693-705, (2007).
• [38] Métivier, R., Gallais, R., Tiffoche, C., Le Péron, C., Jurkowska, R. Z., Carmouche, R. P., ... and Salbert, G. , “Cyclical DNA methylation of a transcriptionally active promoter”, Nature, 452(7183), 45-50, (2008).
• [39] Koçak, E. E., and Ertuğrul, A. , ”Psikiyatrik bozukluklar ve epigenetic”, Türk Psikiyatri Dergisi, 23(2), 130-140, (2012).
• [40] Richards, E. J. , “Inherited epigenetic variation—revisiting soft inheritance”, Nature Reviews Genetics, 7(5), 395-401, (2006).
• [41] Delcuve, G. P., Rastegar, M., and Davie, J. R. , “Epigenetic control”, Journal of cellular physiology, 219(2), 243-250, (2009).
• [42] Kaminsky, Z. A., Tang, T., Wang, S. C., Ptak, C., Oh, G. H., Wong, A. H., ... and Petronis, A. , “DNA methylation profiles in monozygotic and dizygotic twins”, Nature genetics, 41(2), 240-245, (2009).
• [43] Camacho L.A., Universidad Nacional de Colombia Engineering School Computer Systems Engineering (2020), Modeling Epigenetic Evolutionary Algorithms: An approach based on the Epigenetic Regulation process, [online], https://repositorio.unal.edu.co/handle/unal/78751
• [44] National Institute of Environmental Health Sciences NIEHS, (2017) Roadmap epigenomics program, [online] https://www.niehs.nih.gov/research/supported/health/envepi/roadmap/index.cfm
• [45] Genetic Home Reference GHR, (2018) Help me understand genetics, [online] https://ghr.nlm.nih.gov/primer
• [46] Dasgupta, D., and McGregor, D. R. (1993). sGA: A structured genetic algorithm. Glasgow: Department of Computer Science, University of Strathclyde
• [47] Tanev, I., and Yuta, K. , “Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones”, Information Sciences, 178(23), 4469-4481, (2008).
• [48] Periyasamy, S., Gray, A., and Kille, P. (2008) ‘The epigenetic algorithm’, in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence, Hong Kong, China, pp.3228-3236
• [49] Sousa J. and Costa E. (2010) ‘EPIAL - An Epigenetic Approach for an Artificial Life Model’, in Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pp.90-97. DOI: 10.5220/0002732500900097
• [50] Chikumbo, O., Goodman, E., and Deb, K. (2012) ‘Approximating a multi-dimensional Pareto front for a land-use management problem: A modified MOEA with an epigenetic silencing metaphor’, in 2012 IEEE congress on evolutionary computation, Brisbane, QLD, Australia, pp. 1-9
• [51] Turner, A. P., Lones, M. A., Fuente, L. A., Stepney, S., Caves, L. S., and Tyrrell, A. M. , “The incorporation of epigenetics in artificial gene regulatory networks”, BioSystems, 112(2), 56-62, (2013).
• [52] La Cava, W., Spector, L., Danai, K., and Lackner, M. 2014. Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, New York, USA, pp. 141-142
• [53] Rosso, D. H. S. (2018). Bio-inspired computing and smart mobility, Doctoral dissertation, Universidad de Málaga,[online] https://dialnet.unirioja.es/servlet/tesis?codigo=256401
• [54] Stolfi, D. H., & Alba, E. , “Epigenetic algorithms: A New way of building GAs based on epigenetics”, Information Sciences, 424, 250-272, (2018).
• [55] Ricalde, E. (2019) A Genetic programming system with an epigenetic mechanism for traffic signal control [online], https://arxiv.org/abs/1903.03854
• [56] Birogul, S. , ”EpiGenetic algorithm for optimization: Application to mobile network frequency planning”, Arabian Journal for Science and Engineering, 41(3), 883-896, (2016).
• [57] Ezzarii, M., Elghazi, H., El Ghazi, H., and Sadiki, T. (2016) Epigenetic algorithm for performing intrusion detection system. In 2016 International Conference on Advanced Communication Systems and Information Security (ACOSIS), pp. 1-6
• [58] Ezzarii, M., El Ghazi, H., El Ghazi, H., and El Bouanani, F. , ”Epigenetic algorithm-based detection technique for network attacks”, IEEE Access, 8, 199482-199491, (2020).