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.
- 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.
- 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
- Alavi, M. and Henderson, J. C. (1981). An evolutionary strategy for implementing a decision support system. Management science, 27(11):1309–1323.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Boyd, S., Boyd, S. P., and Vandenberghe, L. (2004). Convex optimization. Cambridge university press.10.1017/CBO9780511804441
- 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.
- 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.
- 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.
- Caliński, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, (1):1–27.
- Camp, C. V. and Farshchin, M. (2014). Design of space trusses using modified teaching– learning based optimization. Engineering Structures, 62:87–97.
- Cassady, C. R., Maillart, L. M., and Salman, S. (2005). Ranking sports teams: A customizable quadratic assignment approach. Interfaces, 35(6):497–510.
- Chakraborty, U. K. (2008). Advances in differential evolution, volume 143. Springer.
- 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
- 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.
- Comaniciu, D., Ramesh, V., and Meer, P. (2003). Kernel-based object tracking. IEEE Transactions on pattern analysis and machine intelligence, 25(5):564–577.
- 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
- 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.
- Cordes, V. and Olfman, L. (2016). Sports analytics: predicting athletic performance with a genetic algorithm.
- 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.
- 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.
- Davis, J., Perera, H., and Swartz, T. B. (2015). A simulator for twenty20 cricket. Australian & New Zealand Journal of Statistics, 57(1):55–71.
- Deaven, D. M. and Ho, K.-M. (1995). Molecular geometry optimization with a genetic algorithm. Physical review letters, 75(2):288.
- 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
- Dhiman, G. and Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159:20–50.
- 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.
- 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.
- 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.
- 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
- 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.
- 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
- 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.
- 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.
- 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.
- Freund, Y., Schapire, R., and Abe, N. (1999). A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence, 14(771-780):1612.
- 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.
- 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
- 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.
- Goldberg, D. E. and Samtani, M. P. (1986). Engineering optimization via genetic algorithm. In Electronic computation, pages 471–482. ASCE.
- 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.
- 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
- 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
- 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.
- 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.
- Huning, A. (1976). ARSP: Archiv für Rechts- und Sozialphilosophie / Archives for Philosophy of Law and Social Philosophy, 62(2):298–300.
- 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.
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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
- Koza, J. R. and Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection, volume 1. MIT press.
- Kumarasiri, S. I. (2017). Optimal one day international cricket team selection by genetic algorithm. Journal of Sports Analytics, 3 (4).
- Langdon, W. B. and Harman, M. (2014). Optimizing existing software with genetic programming. IEEE Transactions on Evolutionary Computation, 19(1):118–135.
- 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.
- Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. Norton paperback. W.W. Norton.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- Marcelin, J., Trompette, P., and Dornberger, R. (1995). Optimal structural damping of skis using a genetic algorithm. Structural Optimization, 10(1):67–70.
- 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.
- Maulik, U. and Bandyopadhyay, S. (2000). Genetic algorithm-based clustering technique. Pattern recognition, 33(9):1455–1465.
- 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.
- 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.
- 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
- 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.
- Mirjalili, S., Mirjalili, S. M., and Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69:46–61.
- 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
- 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
- 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.
- 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
- 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
- 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.
- Omkar, S. and Verma, R. (2003). Cricket team selection using genetic algorithm. In International Congress on Sports Dynamics, Melbourne, Australia, pages 1–9. Citeseer.
- 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.
- Ouzzani, M., Hammady, H., Fedorowicz, Z., and Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic Reviews, 5(1):210.
- Parsopoulos, K. E. and Vrahatis, M. N. (2002). Recent approaches to global optimization problems through particle swarm optimization. Natural computing, 1(2):235–306.
- Perera, H., Davis, J., and Swartz, T. B. (2016). Optimal lineups in twenty20 cricket. Journal of Statistical Computation and Simulation, 86(14):2888–2900.
- 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.
- 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.
- 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.
- 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.
- 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.
- Reeves, C. (1996). Hybrid genetic algorithms for bin-packing and related problems. Annals of Operations Research, 63(3):371–396.
- 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
- Rocca, P., Oliveri, G., and Massa, A. (2011). Differential evolution as applied to electromagnetics. IEEE Antennas and Propagation Magazine, 53(1):38–49.
- 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
- 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.
- 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.
- 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.
- 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
- 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
- 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.
- Senthilnath, J., Omkar, S., and Mani, V. (2011). Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 1(3):164–171.
- 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
- 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.
- Shingrakhia, H. and Patel, H. (2020). Emperor penguin optimized event recognition and summarization for cricket highlight generation. Multimedia Systems, 26(6):745–759.
- Silva, R. M. (2016). Sports analytics. PhD thesis, Science: Statistics and Actuarial Science.
- Skinner, B. and Goldman, M. (2015). Optimal strategy in basketball. arXiv preprint arXiv:1512.05652.
- 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).
- 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
- 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.
- Swartz, T. B. (2017). Research directions in cricket. In Handbook of statistical methods and analyses in sports, pages 461–476. Chapman and Hall/CRC.
- 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.
- Takagi, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation. Proceedings of the IEEE, 89(9):1275–1296.
- 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.
- 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.
- 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
- 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.
- Wimbledon. Serena williams pre-tournament press conference — wimbledon 2021.
- Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, (1):67–82.
- Wright, M. B. (2006). Scheduling fixtures for basketball new zealand. Computers & Operations Research, 33(7):1875–1893.
- 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
- Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A. H., and Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: theory and applications. Newnes.
- 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
- Yang, X.-S. and Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4):330–343.
- Yang, X.-S. and He, X. (2013). Firefly algorithm: recent advances and applications. International journal of swarm intelligence, (1):36–50.
- 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