Abstract
This chapter first introduces the motivation for using modern optimization via the R tool. Then, three relevant aspects are discussed: how to represent a solution, how to evaluate the quality of solutions, and how to handle constraints. Next, an overall view of modern optimization methods is presented, followed by a discussion of their limitations and criticism. Finally, this chapter presents the optimization tasks that are used for tutorial purposes in the book.
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References
Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23
Banzhaf W, Nordin P, Keller R, Francone F (1998) Genetic programming, an introduction. Morgan Kaufmann Publishers, Inc., San Francisco
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge
Cass S (2019) The top programming languages 2019. IEEE Spectrum. https://spectrum.ieee.org/computing/software/the-top-programming-languages-2019
Chen WN, Zhang J, Chung HS, Zhong WL, Wu WG, Shi YH (2010) A novel set-based particle swarm optimization method for discrete optimization problems. Evol Comput IEEE Trans 14(2):278–300
Cortez P (2010) Data mining with neural networks and support vector machines using the R/rminer tool. In: Perner P (ed) Advances in data mining – applications and theoretical aspects, 10th industrial conference on data mining. LNAI 6171. Springer, Berlin, pp 572–583
Cortez P, Santos MF (2015) Recent advances on knowledge discovery and business intelligence. Expert Syst 32(3):433–434. https://doi.org/10.1111/exsy.12087
Cortez P, Pereira PJ, Mendes R (2020) Multi-step time series prediction intervals using neuroevolution. Neural Comput Appl 32(13):8939–8953. https://doi.org/10.1007/s00521-019-04387-3
Eberhart R, Kennedy J, Shi Y (2001) Swarm intelligence. Morgan Kaufmann
Eberhart RC, Shi Y (2011) Computational intelligence: concepts to implementations. Morgan Kaufmann
Fay C (2020) Companies, officials and NGO using R. https://github.com/ThinkR-open/companies-using-r
Fernandes G, Oliveira N, Cortez P, Mendes R (2020) A realistic scooter rebalancing system via metaheuristics. In: Coello CAC (ed) GECCO’20: genetic and evolutionary computation conference, companion Volume, CancĂºn, 8–12 July 2020. ACM, pp 265–266. https://doi.org/10.1145/3377929.3389905
Fernandes K, Vinagre P, Cortez P (2015) A proactive intelligent decision support system for predicting the popularity of online news. In: Pereira FC, Machado P, Costa E, Cardoso A (eds) Progress in artificial intelligence – 17th Portuguese conference on artificial intelligence, EPIA 2015, Coimbra, 8–11 Sept 2015. Proceedings. Lecture notes in computer science, vol 9273. Springer, pp 535–546. https://doi.org/10.1007/978-3-319-23485-4_53
Glover F, Laguna M (1998) Tabu search. Springer
Holland J (1975) Adaptation in natural and artificial systems. PhD thesis, University of Michigan, Ann Arbor
Igel C (2014) No free lunch theorems: limitations and perspectives of metaheuristics. In: Borenstein Y, Moraglio A (eds) Theory and principled methods for the design of metaheuristics. Natural computing series. Springer, pp 1–23. https://doi.org/10.1007/978-3-642-33206-7_1
Koch R (2015) From business intelligence to predictive analytics. Strategic Financ 96(7):56
LĂ³pez-IbĂ¡Ă±ez M, Dubois-Lacoste J, CĂ¡ceres LP, Birattari M, StĂ¼tzle T (2016) The irace package: iterated racing for automatic algorithm configuration. Oper Res Perspect 3:43–58
Luke S (2015) Essentials of metaheuristics. Lulu.com, online version 2.2 at http://cs.gmu.edu/~sean/book/metaheuristics
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210. https://doi.org/10.1109/TEVC.2004.826074
Michalewicz Z (2008) Adaptive Business Intelligence, Computer Science Course 7005 Handouts
Michalewicz Z, Fogel D (2004) How to solve it: modern heuristics. Springer, New York
Michalewicz Z, Schmidt M, Michalewicz M, Chiriac C (2006) Adaptive business intelligence. Springer, New York
Muenchen RA (2019) The popularity of data analysis software. http://r4stats.com/articles/popularity/
Parente M, Cortez P, Correia AG (2015) An evolutionary multi-objective optimization system for earthworks. Expert Syst Appl 42(19):6674–6685. https://doi.org/10.1016/j.eswa.2015.04.051
Pereira PJ, Pinto P, Mendes R, Cortez P, Moreau A (2019) Using neuroevolution for predicting mobile marketing conversion. In: Progress in artificial intelligence, 19th EPIA conference on artificial intelligence, EPIA 2019, Vila Real, 3–6 Sept 2019, Proceedings, Part II. Lecture notes in computer science, vol 11805. Springer, pp 373–384. https://doi.org/10.1007/978-3-030-30244-3_31
Quantargo (2020) R is everywhere. https://www.r-bloggers.com/r-is-everywhere/
R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
Rocha M, Cortez P, Neves J (2000) The relationship between learning and evolution in static and in dynamic environments. In: Fyfe C (ed) Proceedings of the 2nd ICSC symposium on engineering of intelligent systems (EIS’2000). ICSC Academic Press, pp 377–383
Rocha M, Mendes R, Cortez P, Neves J (2001) Sitting guest at a wedding party: experiments on genetic and evolutionary constrained optimization. In: Proceedings of the 2001 congress on evolutionary computation (CEC2001), vol 1. IEEE Computer Society, Seoul, pp 671–678
Rocha M, Cortez P, Neves J (2007) Evolution of neural networks for classification and regression. Neurocomputing 70:2809–2816
Rocha M, Sousa P, Cortez P, Rio M (2011) Quality of service constrained routing optimization using evolutionary computation. Appl Soft Comput 11(1):356–364
Ryan C, Collins JJ, O’Neill M (1998) Grammatical evolution: evolving programs for an arbitrary language. In: Banzhaf W, Poli R, Schoenauer M, Fogarty TC (eds) Genetic programming, first European workshop, EuroGP’98, Paris, 14–15 Apr 1998, Proceedings. Lecture notes in computer science, vol 1391. Springer, pp 83–96. https://doi.org/10.1007/BFb0055930
Schrijver A (1998) Theory of linear and integer programming. Wiley, Chichester
Sörensen K (2015) Metaheuristics – the metaphor exposed. ITOR 22(1):3–18. https://doi.org/10.1111/itor.12001
Tang K, Li X, Suganthan P, Yang Z, Weise T (2009) Benchmark functions for the cec’2010 special session and competition on large-scale global optimization. Technical report, University of Science and Technology of China
Turban E, Sharda R, Aronson J, King D (2010) Business intelligence, A managerial approach, 2nd edn. Prentice-Hall
Vance A (2009) R you ready for R? http://bits.blogs.nytimes.com/2009/01/08/r-you-ready-for-r/
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Cortez, P. (2021). Introduction. In: Modern Optimization with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-030-72819-9_1
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DOI: https://doi.org/10.1007/978-3-030-72819-9_1
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