Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms

Ariyaratne, M.K.A.; Silva, R.M.

doi:10.2478/ijcss-2022-0003

Abstract

This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.

References

Ahmed, F., Deb, K., and Jindal, A. (2011a). Evolutionary multi-objective optimization and decision making approaches to cricket team selection. Swarm, Evolutionary, and Memetic Computing. SEMCCO.
Search in Google Scholar Back to article
Ahmed, F., Deb, K., and Jindal, A. (2013). Multi-objective optimization and decision making approaches to cricket team selection. Applied Soft Computing, 13(1):402–414.
Search in Google Scholar Back to article
Ahmed, F., Jindal, A., and Deb, K. (2011b). Cricket team selection using evolutionary multi-objective optimization. In International Conference on Swarm, Evolutionary, and Memetic Computing, pages 71–78. Springer.10.1007/978-3-642-27242-4_9
Search in Google Scholar Back to article
Alavi, M. and Henderson, J. C. (1981). An evolutionary strategy for implementing a decision support system. Management science, 27(11):1309–1323.
Search in Google Scholar Back to article
Balaji, S., Karthikeyan, S., and Manikandan, R. (2021). Object detection using metaheuristic algorithm for volley ball sports application. Journal of Ambient Intelligence and Humanized Computing, 12(1):375–385.
Search in Google Scholar Back to article
Baliarsingh, S. K., Vipsita, S., Muhammad, K., and Bakshi, S. (2019). Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm and Evolutionary Computation, 48:262–273.
Search in Google Scholar Back to article
Bansal, J. C., Sharma, H., Jadon, S. S., and Clerc, M. (2014). Spider monkey optimization algorithm for numerical optimization. Memetic computing, 6(1):31–47.
Search in Google Scholar Back to article
Behravan, I., Zahiri, S. H., Razavi, S. M., and Trasarti, R. (2019). Finding roles of players in football using automatic particle swarm optimization-clustering algorithm. Big data, 7(1):35–56.
Search in Google Scholar Back to article
Biajoli, F. L., Chaves, A., Mine, O., Souza, M., Pontes, R., Lucena, A., and Cabral, L. (2004). Scheduling the brazilian soccer championship: a simulated annealing approach. In Fifth International Conference on the Practice and Theory of Automated Timetabling, Patat2004, Pittsburgh, USA, pages 433–437.
Search in Google Scholar Back to article
Bianchi, L., Dorigo, M., Gambardella, L. M., and Gutjahr, W. J. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2):239–287.
Search in Google Scholar Back to article
Bose, D. and Chakraborty, S. (2019). Managing in-play run chases in limited overs cricket using optimized cusum charts. Journal of Sports Analytics, 5(4):335–346.
Search in Google Scholar Back to article
Boyd, S., Boyd, S. P., and Vandenberghe, L. (2004). Convex optimization. Cambridge university press.10.1017/CBO9780511804441
Search in Google Scholar Back to article
Brettenny, W. J., Friskin, D. G., Gonsalves, J. W., and Sharp, G. D. (2012). A multi-stage integer programming approach to fantasy team selection: a twenty20 cricket study. South African Journal for Research in Sport, Physical Education and Recreation, 3 (1):13–28.
Search in Google Scholar Back to article
Burke, E. K., Newall, J. P., and Weare, R. F. (1995). A memetic algorithm for university exam timetabling. In international conference on the practice and theory of automated timetabling, pages 241–250. Springer.
Search in Google Scholar Back to article
Burney, S. A., Mahmood, N., Rizwan, K., and Amjad, U. (2012). A generic approach for team selection in multi–player games using genetic algorithm. International Journal of Computer Applications, 40(17):11–17.
Search in Google Scholar Back to article
Caliński, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, (1):1–27.
Search in Google Scholar Back to article
Camp, C. V. and Farshchin, M. (2014). Design of space trusses using modified teaching– learning based optimization. Engineering Structures, 62:87–97.
Search in Google Scholar Back to article
Cassady, C. R., Maillart, L. M., and Salman, S. (2005). Ranking sports teams: A customizable quadratic assignment approach. Interfaces, 35(6):497–510.
Search in Google Scholar Back to article
Chakraborty, U. K. (2008). Advances in differential evolution, volume 143. Springer.
Search in Google Scholar Back to article
Cheng, Y., Jiang, M., and Yuan, D. (2009). Novel clustering algorithms based on improved artificial fish swarm algorithm. In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, volume 3, pages 141–145. IEEE.10.1109/FSKD.2009.534
Search in Google Scholar Back to article
Coello, C. A. C., Pulido, G. T., and Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3):256–279.
Search in Google Scholar Back to article
Comaniciu, D., Ramesh, V., and Meer, P. (2003). Kernel-based object tracking. IEEE Transactions on pattern analysis and machine intelligence, 25(5):564–577.
Search in Google Scholar Back to article
Connor, M., Fagan, D., and O’Neill, M. (2019). Optimising team sport training plans with grammatical evolution. In 2019 IEEE Congress on Evolutionary Computation (CEC), pages 2474–2481. IEEE.10.1109/CEC.2019.8790369
Search in Google Scholar Back to article
Connor, M., Faganan, D., Watters, B., McCaffery, F., and O’Neill, M. (2021). Optimizing team sport training with multi-objective evolutionary computation. International Journal of Computer Science in Sport, 20(1):92–105.
Search in Google Scholar Back to article
Cordes, V. and Olfman, L. (2016). Sports analytics: predicting athletic performance with a genetic algorithm.
Search in Google Scholar Back to article
Darwin, C. (1987). Charles Darwin’s natural selection: being the second part of his big species book written from 1856 to 1858. Cambridge University Press.
Search in Google Scholar Back to article
Das, S., Mullick, S. S., and Suganthan, P. N. (2016). Recent advances in differential evolution–an updated survey. Swarm and evolutionary computation, 27:1–30.
Search in Google Scholar Back to article
Davis, J., Perera, H., and Swartz, T. B. (2015). A simulator for twenty20 cricket. Australian & New Zealand Journal of Statistics, 57(1):55–71.
Search in Google Scholar Back to article
Deaven, D. M. and Ho, K.-M. (1995). Molecular geometry optimization with a genetic algorithm. Physical review letters, 75(2):288.
Search in Google Scholar Back to article
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi objective optimization: Nsga-ii. In International conference on parallel problem solving from nature, pages 849–858. Springer.10.1007/3-540-45356-3_83
Search in Google Scholar Back to article
Dhiman, G. and Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159:20–50.
Search in Google Scholar Back to article
Dhiman, G., Oliva, D., Kaur, A., Singh, K. K., Vimal, S., Sharma, A., and Cengiz, K. (2021). Bepo: a novel binary emperor penguin optimizer for automatic feature selection. Knowledge-Based Systems, 211:106560.
Search in Google Scholar Back to article
Dorigo, M. and Gambardella, L. M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1(1):53–66.
Search in Google Scholar Back to article
Espejo, P. G., Ventura, S., and Herrera, F. (2009). A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(2):121–144.
Search in Google Scholar Back to article
Fister, I., Brest, J., Iglesias, A., and Fister Jr, I. (2018). Framework for planning the training sessions in triathlon. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1829–1834.10.1145/3205651.3208242
Search in Google Scholar Back to article
Fister, I., Fister, D., Deb, S., Mlakar, U., and Brest, J. (2020). Post hoc analysis of sport performance with differential evolution. Neural Computing and Applications, 32(15):10799–10808.
Search in Google Scholar Back to article
Fister, I., Iglesias, A., Deb, S., and Fister, D. (2017). Modeling preference time in middle distance triathlons. In 2017 5th International Symposium on Computational and Business Intelligence (ISCBI), pages 65–69. IEEE.10.1109/ISCBI.2017.8053546
Search in Google Scholar Back to article
Fister, I., Rauter, S., Yang, X.-S., Ljubič, K., and Fister Jr, I. (2015). Planning the sports training sessions with the bat algorithm. Neurocomputing, 149:993–1002.
Search in Google Scholar Back to article
Fister Jr, I., Fister, D., Deb, S., Mlakar, U., Brest, J., and Fister, I. (2017). Making up for the deficit in a marathon run. In Proceedings of the 2017 international conference on intelligent systems, metaheuristics & swarm intelligence, pages 11–15.
Search in Google Scholar Back to article
Fister Jr, I., Ljubič, K., Suganthan, P. N., Perc, M., and Fister, I. (2015). Computational intelligence in sports: challenges and opportunities within a new research domain. Applied Mathematics and Computation, 262:178–186.
Search in Google Scholar Back to article
Freund, Y., Schapire, R., and Abe, N. (1999). A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence, 14(771-780):1612.
Search in Google Scholar Back to article
Gao, Y., Guan, H., Qi, Z., Hou, Y., and Liu, L. (2013). A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. Journal of computer and system sciences, 79(8):1230–1242.
Search in Google Scholar Back to article
Geng, S. and Hu, T. (2020). Sports games modeling and prediction using genetic programming. In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–6. IEEE.10.1109/CEC48606.2020.9185917
Search in Google Scholar Back to article
Goffe, W. L., Ferrier, G. D., and Rogers, J. (1994). Global optimization of statistical functions with simulated annealing. Journal of econometrics, 60(1-2):65–99.
Search in Google Scholar Back to article
Goldberg, D. E. and Samtani, M. P. (1986). Engineering optimization via genetic algorithm. In Electronic computation, pages 471–482. ASCE.
Search in Google Scholar Back to article
Gomez, J., Khodr, H., De Oliveira, P., Ocque, L., Yusta, J., Villasana, R., and Urdaneta, A. (2004). Ant colony system algorithm for the planning of primary distribution circuits. IEEE Transactions on power systems, 19(2):996–1004.
Search in Google Scholar Back to article
Guangdong, H., Ping, L., and Qun, W. (2007). A hybrid metaheuristic aco-ga with an application in sports competition scheduling. In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), volume 3, pages 611–616. IEEE.10.1109/SNPD.2007.402
Search in Google Scholar Back to article
Han, S. (2012). Batting order optimization by genetic algorithm. In Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, pages 599–602.10.1145/2330784.2330882
Search in Google Scholar Back to article
Hayes-Roth, F. (1975). Review of” adaptation in natural and artificial systems by john h. holland”, the u. of michigan press, 1975. ACM SIGART Bulletin, (53):15–15.
Search in Google Scholar Back to article
Houck, C. R., Joines, J., and Kay, M. G. (1995). A genetic algorithm for function optimization: a matlab implementation. Ncsu-ie tr, 95(09):1–10.
Search in Google Scholar Back to article
Huning, A. (1976). ARSP: Archiv für Rechts- und Sozialphilosophie / Archives for Philosophy of Law and Social Philosophy, 62(2):298–300.
Search in Google Scholar Back to article
Ilonen, J., Kamarainen, J.-K., and Lampinen, J. (2003). Differential evolution training algorithm for feed-forward neural networks. Neural Processing Letters, 17(1):93–105.
Search in Google Scholar Back to article
Jain, M., Singh, V., and Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and evolutionary computation, 44:148–175.
Search in Google Scholar Back to article
Jana, A. and Hemalatha, S. (2021). Football player performance analysis using particle swarm optimization and player value calculation using regression. In Journal of Physics: Conference Series, volume 1911, page 012011. IOP Publishing.
Search in Google Scholar Back to article
Kamble, A., Rao, R. V., Kale, A., and Samant, S. (2011). Selection of cricket players using analytical hierarchy process. International Journal of Sports Science and Engineering, 5(4):207–212.
Search in Google Scholar Back to article
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks, volume 4, pages 1942–1948. IEEE.10.1109/ICNN.1995.488968
Search in Google Scholar Back to article
Khemka, N., Jacob, C., and Cole, G. (2005). Making soccer kicks better: a study in particle swarm optimization and evolution strategies. In 2005 IEEE Congress on Evolutionary Computation, volume 1, pages 735–742. IEEE.10.1109/CEC.2005.1554756
Search in Google Scholar Back to article
Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1987). Optimization by simulated annealing. In Readings in Computer Vision, pages 606–615. Elsevier.10.1016/B978-0-08-051581-6.50059-3
Search in Google Scholar Back to article
Knowles, J. D. and Corne, D. W. (2000). M-paes: A memetic algorithm for multiobjective optimization. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), volume 1, pages 325–332. IEEE.10.1109/CEC.2000.870313
Search in Google Scholar Back to article
Koza, J. R. and Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press.
Search in Google Scholar Back to article
Kumarasiri, S. I. (2017). Optimal one day international cricket team selection by genetic algorithm. Journal of Sports Analytics, 3 (4).
Search in Google Scholar Back to article
Langdon, W. B. and Harman, M. (2014). Optimizing existing software with genetic programming. IEEE Transactions on Evolutionary Computation, 19(1):118–135.
Search in Google Scholar Back to article
Lee, S.-H., Jung, Y., Moon, H.-W., and Woo, Y.-T. (2019). A baseball batter evaluation model using genetic algorithm. Journal of The Korea Society of Computer and Information, 24(1):41–47.
Search in Google Scholar Back to article
Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. Norton paperback. W.W. Norton.
Search in Google Scholar Back to article
Li, J. and Wang, W. (2011). Extracting impact characteristics of sports training on eeg by genetic algorithm. In 2011 First International Workshop on Complexity and Data Mining, pages 76–79. IEEE.10.1109/IWCDM.2011.48
Search in Google Scholar Back to article
Lim, A., Rodrigues, B., and Zhang, X. (2006). A simulated annealing and hill-climbing algorithm for the traveling tournament problem. European Journal of Operational Research, 174(3):1459–1478.
Search in Google Scholar Back to article
Lü, Z. and Hao, J.-K. (2010). A memetic algorithm for graph coloring. European Journal of Operational Research, 203(1):241–250. Luke, S. and Spector, L. (1997). A comparison of crossover and mutation in genetic programming. Genetic Programming, 97:240–248.
Search in Google Scholar Back to article
Manafifard, M., Ebadi, H., and Abrishami Moghaddam, H. (2015). Discrete particle swarm optimization for player trajectory extraction in soccer broadcast videos. Scientia Iranica, 22(3):1031–1044.
Search in Google Scholar Back to article
Manafifard, M., Ebadi, H., and Moghaddam, H. A. (2017). Multi-player detection in soccer broadcast videos using a blob-guided particle swarm optimization method. Multimedia Tools and Applications, 76(10):12251–12280.
Search in Google Scholar Back to article
Marano, G. C., Quaranta, G., and Monti, G. (2011). Modified genetic algorithm for the dynamic identification of structural systems using incomplete measurements. Computer-Aided Civil and Infrastructure Engineering, 26(2):92–110.
Search in Google Scholar Back to article
Marcelin, J., Trompette, P., and Dornberger, R. (1995). Optimal structural damping of skis using a genetic algorithm. Structural Optimization, 10(1):67–70.
Search in Google Scholar Back to article
Marcelino, R., Sampaio, J., Amichay, G., Gonçalves, B., Couzin, I. D., and Nagy, M. (2020). Collective movement analysis reveals coordination tactics of team players in football matches. Chaos, Solitons & Fractals, 138:109831.
Search in Google Scholar Back to article
Maulik, U. and Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Pattern recognition, 33(9):1455–1465.
Search in Google Scholar Back to article
Mazloomi, M. S. and Evans, P. D. (2021). Shape optimization of a wooden baseball bat using parametric modeling and genetic algorithms. AI, 2(3):381–393.
Search in Google Scholar Back to article
Mazloomi, M. S., Saadatfar, M., and Evans, P. D. (2020). Designing cricket bats using parametric modeling and genetic algorithms. Wood Science and Technology, 54(3):755–768.
Search in Google Scholar Back to article
McHutchon, M., Manson, G., and Carré, M. (2006). A fresh approach to sports equipment design: Evolving hockey sticks using genetic algorithms. In The Engineering of Sport 6, pages 81–86. Springer.10.1007/978-0-387-45951-6_15
Search in Google Scholar Back to article
Mester, D., Ronin, Y., Minkov, D., Nevo, E., and Korol, A. (2003). Constructing large-scale genetic maps using an evolutionary strategy algorithm. Genetics, 165(4):2269–2282.
Search in Google Scholar Back to article
Mirjalili, S., Mirjalili, S. M., and Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69:46–61.
Search in Google Scholar Back to article
Mlakar, M. and Luštrek, M. (2017). Analyzing tennis game through sensor data with machine learning and multi- objective optimization. In Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers, pages 153–156.10.1145/3123024.3123163
Search in Google Scholar Back to article
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. BMJ, 339.10.1136/bmj.b2535271465719622551
Search in Google Scholar Back to article
Nakane, T., Bold, N., Sun, H., Lu, X., Akashi, T., and Zhang, C. (2020). Application of evolutionary and swarm optimization in computer vision: a literature survey. IPSJ Transactions on Computer Vision and Applications, 12(1):1–34.
Search in Google Scholar Back to article
Narasimhan, H., Satheesh, S., and Sriram, D. (2010). Automatic summarization of cricket video events using genetic algorithm. In Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, pages 2051–2054.10.1145/1830761.1830858
Search in Google Scholar Back to article
Nelikanti, A., Reddy, G. V. R., and Karuna, G. (2021). An optimization based deep lstm predictive analysis for decision making in cricket. In Innovative Data Communication Technologies and Application, pages 721–737. Springer.10.1007/978-981-15-9651-3_59
Search in Google Scholar Back to article
Neshat, M., Sepidnam, G., Sargolzaei, M., and Toosi, A. N. (2014). Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial intelligence review, 42(4):965–997.
Search in Google Scholar Back to article
Omkar, S. and Verma, R. (2003). Cricket team selection using genetic algorithm. In International Congress on Sports Dynamics, Melbourne, Australia, pages 1–9. Citeseer.
Search in Google Scholar Back to article
Ouaarab, A., Ahiod, B., and Yang, X.-S. (2014). Discrete cuckoo search algorithm for the travelling salesman problem. Neural Computing and Applications, 24(7):1659–1669.
Search in Google Scholar Back to article
Ouzzani, M., Hammady, H., Fedorowicz, Z., and Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5(1):210.
Search in Google Scholar Back to article
Parsopoulos, K. E. and Vrahatis, M. N. (2002). Recent approaches to global optimization problems through particle swarm optimization. Natural computing, 1(2):235–306.
Search in Google Scholar Back to article
Perera, H., Davis, J., and Swartz, T. B. (2016). Optimal lineups in twenty20 cricket. Journal of Statistical Computation and Simulation, 86(14):2888–2900.
Search in Google Scholar Back to article
Pérez-Toledano, M. Á., Rodriguez, F. J., Garćıa-Rubio, J., and Ibañez, S. J. (2019). Players’ selection for basketball teams, through performance index rating, using multiobjective evolutionary algorithms. PloS one, 14(9):e0221258.
Search in Google Scholar Back to article
Prakash, C. D. (2016). A new team selection methodology using machine learning and memetic genetic algorithm for ipl-9. Int. Jl. of Electronics, Electrical and Computational System IJEECS ISSN.
Search in Google Scholar Back to article
QIAN, X. L. L. J. S. X. (2002). An optimizing method based on autonomous animats: Fish-swarm algorithm. Systems Engineering-Theory and Practice, 22(11):32.
Search in Google Scholar Back to article
Rao, R. V. and Kalyankar, V. D. (2013). Parameter optimization of modern machining processes using teaching– learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(1):524–531.
Search in Google Scholar Back to article
Rao, R. V., Savsani, V. J., and Vakharia, D. (2011). Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3):303–315.
Search in Google Scholar Back to article
Reeves, C. (1996). Hybrid genetic algorithms for bin-packing and related problems. Annals of Operations Research, 63(3):371–396.
Search in Google Scholar Back to article
Robič, T. and Filipič, B. (2005). Differential evolution for multiobjective optimization. In International conference on evolutionary multi-criterion optimization, pages 520–533. Springer.10.1007/978-3-540-31880-4_36
Search in Google Scholar Back to article
Rocca, P., Oliveri, G., and Massa, A. (2011). Differential evolution as applied to electromagnetics. IEEE Antennas and Propagation Magazine, 53(1):38–49.
Search in Google Scholar Back to article
Romero, F. P., Lozano-Murcia, C., Lopez-Gomez, J. A., Angulo Sanchez-Herrera, E., and Sanchez-Lopez, E. (2021). A data-driven approach to predicting the most valuable player in a game. Computational and Mathematical Methods, page e1155.10.1002/cmm4.1155
Search in Google Scholar Back to article
Rotshtein, A. P., Posner, M., and Rakityanskaya, A. (2005). Football predictions based on a fuzzy model with genetic and neural tuning. Cybernetics and Systems Analysis, 41(4):619–630.
Search in Google Scholar Back to article
Roubos, J., Van Straten, G., and Van Boxtel, A. (1999). An evolutionary strategy for fed-batch bioreactor optimization; concepts and performance. Journal of Biotechnology, 67(2-3):173–187.
Search in Google Scholar Back to article
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical science, 4(4):409–423.
Search in Google Scholar Back to article
Sathya, S. S. and Jamal, M. S. (2009). Applying genetic algorithm to select an optimal cricket team. In Proceedings of the International Conference on Advances in Computing, Communication and Control, pages 43–47.10.1145/1523103.1523113
Search in Google Scholar Back to article
Schaefer, D., Asteroth, A., and Ludwig, M. (2015). Training plan evolution based on training models. In 2015 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), pages 1–8. IEEE.10.1109/INISTA.2015.7276739
Search in Google Scholar Back to article
Schönberger, J., Mattfeld, D. C., and Kopfer, H. (2004). Memetic algorithm timetabling for non-commercial sport leagues. European Journal of Operational Research, 153(1):102–116.
Search in Google Scholar Back to article
Senthilnath, J., Omkar, S., and Mani, V. (2011). Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 1(3):164–171.
Search in Google Scholar Back to article
Shan, G. (2008). Sport equipment evaluation and optimization–a review of the relationship between sport science research and engineering. The Open Sports Sciences Journal, 1(1).10.2174/1875399X00801010005
Search in Google Scholar Back to article
Shimoyama, K., Seo, K., Nishiwaki, T., Jeong, S., and Obayashi, S. (2011). Design optimization of a sport shoe sole structure by evolutionary computation and finite element method analysis. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 225(4):179–188.
Search in Google Scholar Back to article
Shingrakhia, H. and Patel, H. (2020). Emperor penguin optimized event recognition and summarization for cricket highlight generation. Multimedia Systems, 26(6):745–759.
Search in Google Scholar Back to article
Silva, R. M. (2016). Sports analytics. PhD thesis, Science: Statistics and Actuarial Science.
Search in Google Scholar Back to article
Skinner, B. and Goldman, M. (2015). Optimal strategy in basketball. arXiv preprint arXiv:1512.05652.
Search in Google Scholar Back to article
Skiscim, C. C. and Golden, B. L. (1983). Optimization by simulated annealing: A preliminary computational study for the tsp. Technical report, Institute of Electrical and Electronics Engineers (IEEE).
Search in Google Scholar Back to article
Storn, R. (1996). On the usage of differential evolution for function optimization. In Proceedings of North American Fuzzy Information Processing, pages 519–523. IEEE.10.1109/NAFIPS.1996.534789
Search in Google Scholar Back to article
Storn, R. and Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4):341–359.
Search in Google Scholar Back to article
Swartz, T. B. (2017). Research directions in cricket. In Handbook of statistical methods and analyses in sports, pages 461–476. Chapman and Hall/CRC.
Search in Google Scholar Back to article
Swartz, T. B., Gill, P. S., Beaudoin, D., and DeSilva, B. M. (2006). Optimal batting orders in one-day cricket. Computers & operations research, 33(7):1939–1950.
Search in Google Scholar Back to article
Takagi, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation. Proceedings of the IEEE, 89(9):1275–1296.
Search in Google Scholar Back to article
Tsakonas, A., Dounias, G., Shtovba, S., and Vivdyuk, V. (2002). Soft computing-based result prediction of football games. In The First International Conference on Inductive Modelling (ICIM’2002). Lviv, Ukraine. Citeseer.
Search in Google Scholar Back to article
Wang, H., Qu, W., and Shen, Q. (2014). Table tennis video data mining based on performance optimization of artificial fish swarm algorithm. Computer Modelling and New Technologies, 18(12):584–588.
Search in Google Scholar Back to article
Weimer, W., Nguyen, T., Le Goues, C., and Forrest, S. (2009). Automatically finding patches using genetic programming. In 2009 IEEE 31st International Conference on Software Engineering, pages 364–374. IEEE.10.1109/ICSE.2009.5070536
Search in Google Scholar Back to article
Willis, R. J. and Terrill, B. J. (1994). Scheduling the australian state cricket season using simulated annealing. Journal of the Operational Research Society, 45(3):276–280.
Search in Google Scholar Back to article
Wimbledon. Serena williams pre-tournament press conference — wimbledon 2021.
Search in Google Scholar Back to article
Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, (1):67–82.
Search in Google Scholar Back to article
Wright, M. B. (2006). Scheduling fixtures for basketball new zealand. Computers & Operations Research, 33(7):1875–1893.
Search in Google Scholar Back to article
Yang, X.-S. (2009). Firefly algorithms for multimodal optimization. In International symposium on stochastic algorithms, pages 169 178. Springer.10.1007/978-3-642-04944-6_14
Search in Google Scholar Back to article
Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A. H., and Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: theory and applications. Newnes.
Search in Google Scholar Back to article
Yang, X.-S. and Deb, S. (2009). Cuckoo search via lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC), pages 210–214. IEEE.10.1109/NABIC.2009.5393690
Search in Google Scholar Back to article
Yang, X.-S. and Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4):330–343.
Search in Google Scholar Back to article
Yang, X.-S. and He, X. (2013). Firefly algorithm: recent advances and applications. International journal of swarm intelligence, (1):36–50.
Search in Google Scholar Back to article
Zhao, G. (2008). Event-based soccer video retrieval with interactive genetic algorithm. In 2008 International Symposium on Information Science and Engineering, volume 2, pages 338–345. IEEE.10.1109/ISISE.2008.94
Search in Google Scholar Back to article

Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms

Abstract

Paradigm

My account