Top

Artificial Intelligence Review

Published in:

01-08-2020

Evolutionary computation for solving search-based data analytics problems

Authors: Shi Cheng, Lianbo Ma, Hui Lu, Xiujuan Lei, Yuhui Shi

Published in: Artificial Intelligence Review | Issue 2/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Automatic extracting of knowledge from massive data samples, i.e., big data analytics (BDA), has emerged as a vital task in almost all scientific research fields. The BDA problems are rather difficult to solve due to their large-scale, high-dimensional, and dynamic properties, while the problems with small data are usually hard to handle due to insufficient data samples and incomplete information. Such difficulties lead to the search-based data analytics problem, where a data analysis task is modeled as a complex, dynamic, and computationally expensive optimization problem and then solved by using an iterative algorithm. In this paper, we intend to present an extensive and in-depth discussion on the utilizing of evolutionary computation (EC) based optimization methods [including evolutionary algorithms (EAs) and swarm intelligence (SI)] for solving search-based data analysis problems. Then, as an example for illustration, we provide a comprehensive review of the applications of state-of-the-art EC methods for different types of data mining problems in bioinformatics. Here, the detailed analysis and discussion are conducted on three types of data samples, which include sequences data, network data, and image data. Finally, we survey the challenges faced by EC methods and the trend for future directions. Based on the applications of EC methods for search-based data analysis problems involving inexact and uncertain information, the insights of data analytics are able to understand better, and more efficient algorithms could be designed to solve real-world complex BDA problems.

previous article PROMETHEE II method based on variable precision fuzzy rough sets with fuzzy neighborhoods

next article Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Abraham A, Grosan C, Ramos V (eds) (2006) Swarm intelligence in data mining, studies in computational intelligence, vol 34. Springer, Berlin/HeidelbergMATH

Amaran S, Sahinidis NV, Sharda B, Bury SJ (2016) Simulation optimization: a review of algorithms and applications. Ann Oper Res 240(1):351–380. https://doi.org/10.1007/s10479-015-2019-xMathSciNetCrossRefMATH

Ashlock W, Datta S (2013) Evolved features for DNA sequence classification and their fitness landscapes. IEEE Trans Evol Comput 17(2):185–197CrossRef

Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach, 2nd edn. Adaptive computation and machine learning series. The MIT Press, CambridgeMATH

Beyer HG, Schwefel HP (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52MathSciNetMATHCrossRef

Bhandarkar SM, Zhang H (1999) Image segmentation using evolutionary computation. IEEE Trans Evol Comput 3(1):1–21. https://doi.org/10.1109/4235.752917CrossRef

Bhattacharya M, Islam R, Abawajy J (2016) Evolutionary optimization: a big data perspective. J Netw Comput Appl 59:416–426. https://doi.org/10.1016/j.jnca.2014.07.032CrossRef

Bi C (2008) Evolutionary metropolis sampling in sequence alignment space. In: Proceedings of the 2008 IEEE congress on evolutionary computation (CEC 2008), pp 189–194. https://doi.org/10.1109/CEC.2008.4630797

Bi C (2009) A Monte Carlo EM algorithm for De Novo motif discovery in biomolecular sequences. IEEE/ACM Trans Comput Biol Bioinf 6(3):370–386. https://doi.org/10.1109/TCBB.2008.103MathSciNetCrossRef

Bidar M, Mouhoub M (2019) Constraint solving and optimization using evolutionary techniques. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence (IJCAI-19), pp 6424–6425. https://doi.org/10.24963/ijcai.2019/901

Bierlaire M (2015) Simulation and optimization: a short review. Transp Res Part C Emerg Technol 55:4–13. https://doi.org/10.1016/j.trc.2015.01.004CrossRef

Blagus R, Lusa L (2013) SMOTE for high-dimensional class-imbalanced data. BMC Bioinform 14:1–16. https://doi.org/10.1186/1471-2105-14-106CrossRef

Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Recent advances and emerging challenges of feature selection in the context of big data. Knowl-Based Syst 86:33–45. https://doi.org/10.1016/j.knosys.2015.05.014CrossRef

Bosman PAN, Thierens D (2003) The balance between proximity and diversity in multi-objective evolutionary algorithms. IEEE Trans Evol Comput 7(2):174–188CrossRef

Brockhoff D, Zitzler E (2009) Objective reduction in evolutionary multi-objective optimization: theory and applications. Evol Comput 17(2):135–166CrossRef

Bui LT, Michalewicz Z, Parkinson E, Abello MB (2012) Adaptation in dynamic environments: a case study in mission planning. IEEE Trans Evol Comput 16(2):190–209CrossRef

Cannataro M (2018) Big data analysis in bioinformatics. Springer International Publishing, Cham, pp 1–19. https://doi.org/10.1007/978-3-319-63962-8_139-1CrossRef

Carrasco J, García S, Rueda MM, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comput 54:100,665. https://doi.org/10.1016/j.swevo.2020.100665

Chai T, Jin Y, Sendhoff B (2013) Evolutionary complex engineering optimization: opportunities and challenges. IEEE Comput Intell Mag 8(3):12–15CrossRef

Chan TM, Leung KS, Lee KH (2012) Memetic algorithms for De Novo motif discovery. IEEE Trans Evol Comput 16(5):730–748CrossRef

Chen R, Li K, Yao X (2018) Dynamic multi-objectives optimization with a changing number of objectives. IEEE Trans Evol Comput 22(1):157–171. https://doi.org/10.1109/TEVC.2017.2669638CrossRef

Chen T, Tang K, Chen G, Yao X (2010) Analysis of computational time of simple estimation of distribution algorithms. IEEE Trans Evol Comput 14(1):1–22. https://doi.org/10.1109/TEVC.2009.2040019CrossRef

Chen XW, Gao JX (2016) Big data bioinformatics. Methods 111:1–2. https://doi.org/10.1016/j.ymeth.2016.11.017CrossRef

Cheng R, Li M, Yao X (2017) Parallel peaks: a visualization method for benchmark studies of multimodal optimization. In: 2017 IEEE congress on evolutionary computation (CEC), pp 263–270. https://doi.org/10.1109/CEC.2017.7969322

Cheng S, Liu B, Shi Y, Jin Y, Li B (2016) Evolutionary computation and big data: key challenges and future directions. In: Tan Y, Shi Y (eds) Data mining and big data, Lecture Notes in Computer Science, vol 9714, pp 3–14. Springer International Publishing Switzerland

Cheng S, Liu B, Ting TO, Qin Q, Shi Y, Huang K (2016b) Survey on data science with population-based algorithms. Big Data Anal 1:1–20. https://doi.org/10.1186/s41044-016-0003-3CrossRef

Cheng S, Lu H, Guo Yn, Lei X, Liang J, Chen J, Shi Y (2019) Dynamic multimodal optimization: a preliminary study. In: Proceedings of 2019 IEEE congress on evolutionary computation (CEC 2019). IEEE, pp 279–285. https://doi.org/10.1109/CEC.2019.8790078

Cheng S, Lu H, Song W, Chen J, Shi Y (2018) Dynamic multimodal optimization using brain storm optimization algorithms. In: Bio-inspired computing: theories and applications, pp 236–245. Springer Singapore, Singapore. https://doi.org/10.1007/978-981-13-2826-8_21

Cheng S, Shi Y (2019) Brain storm optimization algorithms: concepts, principles and applications, adaptation, learning, and optimization, vol 23. Springer International Publishing AG. https://doi.org/10.1007/978-3-030-15070-9

Cheng S, Shi Y, Qin Q, Gao S (2013) Solution clustering analysis in brain storm optimization algorithm. In: Proceedings of the 2013 IEEE symposium on swarm intelligence (SIS 2013). IEEE, Singapore, pp 111–118

Cheng S, Zhang Q, Qin Q (2016c) Big data analytics with swarm intelligence. Ind Manag Data Syst 116(4):646–666. https://doi.org/10.1108/IMDS-06-2015-0222CrossRef

Chetty M, Ngom A, Marchiori E (2010) Editorial: computational intelligence in bioinformatics. Neurocomputing 73:2291–2292CrossRef

Cios KJ, Mamitsuka H, Nagashima T, Tadeusiewicz R (2005) Guest editorial: computational intelligence in solving bioinformatics problems. Artif Intell Med 35:1–8CrossRef

Coello Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Genetic and Evolutionary Computation Series. Springer

Coello Coello CA, Dehuri S, Ghosh S (eds) (2009) Swarm intelligence for multi-objective problems in data mining, vol 242. Studies in Computational Intelligence. Springer, Berlin/HeidelbergMATH

Cohen J (2004) Bioinformatics—an introduction for computer scientists. ACM Comput Surv 36(2):122–158CrossRef

Cohen J (2005) Computer science and bioinformatics. Commun ACM 48(3):72–78CrossRef

Cohen SCM, de Castro LN (2006) Data clustering with particle swarms. In: Proceedings of the 2006 IEEE congress on evolutionary computations (CEC 2006), pp 1792–1798

Correa ES, Freitas AA, Johnson CG (2006) A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set. In: Genetic and evolutionary computation conference (GECCO 2006) (pp 35–42). ACM, Seattle, Washington, USA

Coutinho F, Ogasawara E, de Oliveira D, Braganholo V, Lima AAB, Dávila AMR, Mattoso M (2016) Data parallelism in bioinformatics workflows using hydra. In: The ACM international symposium on high performance distributed computing (HPDC 2010) (pp 507–515). ACM, Chicago, Illinois, USA

Crawford VG (2019) An efficient evolutionary algorithm for minimum cost submodular cover. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence (IJCAI–19), pp 1227–1233. International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2019/171

Cuevas E, Zaldívar D, Perez-Cisneros M (2016) Applications of evolutionary computation in image processing and pattern recognition, vol 100. Intelligent Systems Reference Library. Springer International Publishing, ChamMATHCrossRef

Damas S, Cordón O, Santamaría J (2011) Medical image registration using evolutionary computation: an experimental survey. IEEE Comput Intell Mag 6(4):26–42. https://doi.org/10.1109/MCI.2011.942582CrossRef

Darwish A, Hassanien AE, Das S (2020) A survey of swarm and evolutionary computing approaches for deep learning. Artif Intell Rev 53:1767–1812. https://doi.org/10.1007/s10462-019-09719-2CrossRef

Das AK, Das S, Ghosh A (2017) Ensemble feature selection using bi-objective genetic algorithm. Knowl-Based Syst 123:116–127. https://doi.org/10.1016/j.knosys.2017.02.013CrossRef

De Bufala N, Kant JD (2019) An evolutionary approach to find optimal policies with an agent-based simulation. In: Proceedings of the 18th international conference on autonomous agents and multiagent systems (AAMAS 2019), pp 610–618. https://doi.org/10.5555/3306127.3331747

Del Ser J, Osaba E, Molina D, Yang XS, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello Coello CA, Herrera F (2019) Bio-inspired computation: Where we stand and what’s next. Swarm Evol Comput 48:220–250. https://doi.org/10.1016/j.swevo.2019.04.008CrossRef

D’haeseleer P (2006) What are DNA sequence motifs? Nat Biotechnol 24(4):423–425. https://doi.org/10.1038/nbt0406-423CrossRef

Dhar V (2013) Data science and prediction. Commun ACM 56(12):64–73CrossRef

Ding J, Yang C, Jin Y, Chai T (2019) Generalized multitasking for evolutionary optimization of expensive problems. IEEE Trans Evol Comput 23(1):44–58. https://doi.org/10.1109/TEVC.2017.2785351CrossRef

Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87CrossRef

Donoho DL (2000) Aide-Memoire. High-dimensional data analysis: the curses and blessings of dimensionality. Tech. rep., Stanford University

Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRef

Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, CambridgeMATHCrossRef

Du Y, Wang T, Xin B, Wang L, Chen Y, Xing L (2019) A data-driven parallel scheduling approach for multiple agile earth observation satellites. IEEE Trans Evol Comput p. in press. https://doi.org/10.1109/TEVC.2019.2934148

Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–54

Feng L, Gupta A, Ong YS (2019) Compressed representation for higher-level meme space evolution: a case study on big knapsack problems. Memetic Comput 11(1):3–17. https://doi.org/10.1007/s12293-017-0244-3CrossRef

Fernández A, del Río S, Chawla NV, Herrera F (2017) An insight into imbalanced big data classification: outcomes and challenges. Complex Intell Syst 3(2):105–120. https://doi.org/10.1007/s40747-017-0037-9CrossRef

Ficici SG (2005) Monotonic solution concepts in coevolution. In: Genetic and evolutionary computation conference (GECCO 2005), pp 499–506

Fogel GB, Corne DW (2003) Editorial: computational intelligence in bioinformatics. BioSystems 72:1–4CrossRef

Fogel GB, Corne DW (2003) Evolutionary computation in bioinformatics. Morgan Kaufmann Publishers, San Francisco

Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New YorkMATH

Friedrich T, Oliveto PS, Sudholt D, Witt C (2009) Analysis of diversity-preserving mechanisms for global exploration. Evol Comput 17(4):455–476. https://doi.org/10.1162/evco.2009.17.4.17401CrossRef

Garbelini JC, Kashiwabara AY, Sanches DS (2016) Discovery motifs by evolutionary computation. In: Genetic and evolutionary computation conference (GECCO 2016 Companion) (pp 1463–1464). ACM, Denver, Colorado, USA

García S, Herrera F (2009) Evolutionary under sampling for classification with imbalanced datasets: Proposals and taxonomy. Evol Comput 17(3):275–306. https://doi.org/10.1162/evco.2009.17.3.275MathSciNetCrossRef

Ge H, Zhao M, Sun L, Wang Z, Tan G, Zhang Q, Chen CLP (2019) A many-objective evolutionary algorithm with two interacting processes: Cascade clustering and reference point incremental learning. IEEE Trans Evol Comput 23(4):572–586. https://doi.org/10.1109/TEVC.2018.2874465CrossRef

Gibbs MS, Maier HR, Dandy GC (2011) Relationship between problem characteristics and the optimal number of genetic algorithm generations. Eng Optim 43(4):349–376. https://doi.org/10.1080/0305215x.2010.491547CrossRef

González-Álvarez DL, Vega-Rodríguez MA, Rubio-Largo A (2015) Multi-objective optimization algorithms for motif discovery in DNA sequences. Genet Program Evolvable Mach 16(2):167–209CrossRef

Hallen MA, Donald BR (2019) Protein design by provable algorithms. Commun ACM 62(10):76–84. https://doi.org/10.1145/3338124CrossRef

Handl J, Kell DB, Knowles J (2007) Multi-objective optimization in bioinformatics and computational biology. IEEE/ACM Trans Comput Biol Bioinf 4(2):279–292CrossRef

Hassanien AE, Al-Shammari ET, Ghali NI (2013) Computational intelligence techniques in bioinformatics. Comput Biol Chem 47:37–47CrossRef

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer Series in Statistics. Springer

Hauschild M, Pelikan M (2011) An introduction and survey of estimation of distribution algorithms. Swarm Evol Comput 1(3):111–128CrossRef

He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284. https://doi.org/10.1109/TKDE.2008.239CrossRef

Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. The MIT Press, CambridgeMATH

Hu X, Pan Y (2007) Knowledge discovery in bioinformatics: techniques, methods, and applications. Wiley, HobokenCrossRef

Iqbal M, Xue B, Al-Sahaf H, Zhang M (2017) Cross-domain reuse of extracted knowledge in genetic programming for image classification. IEEE Trans Evol Comput 21(4):569–587. https://doi.org/10.1109/TEVC.2017.2657556CrossRef

Irfan S, Babu BV (2016) Information retrieval in big data using evolutionary computation: a survey. In: 2016 International conference on computing, communication and automation (ICCCA), pp 208–213. https://doi.org/10.1109/CCAA.2016.7813720

Ishibuchi H, Peng Y, Shang K (2019) A scalable multi-modal multi-objective test problem. In: 2019 IEEE congress on evolutionary computation (CEC), pp 310–317. https://doi.org/10.1109/CEC.2019.8789971

Ishibuchi H, Tsukamoto N, Nojima Y (2008) Evolutionary many-objective optimization: a short review. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), pp 2419–2426. https://doi.org/10.1109/CEC.2008.4631121

Jiang S, Yang S (2017) Evolutionary dynamic multi-objective optimization: benchmarks and algorithm comparisons. IEEE Trans Cybern 47(1):198–211. https://doi.org/10.1109/TCYB.2015.2510698CrossRef

Jin Y (2016) Data driven evolutionary optimization of complex systems: big data versus small data. In: Proceedings of the 2016 on genetic and evolutionary computation conference companion. ACM, pp 1281–1282. https://doi.org/10.1145/2908961.2931715

Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3):303–317CrossRef

Jin Y, Hammer B (2014) Computational intelligence in big data. IEEE Comput Intell Mag 9(3):12–13CrossRef

Jin Y, Sendhoff B (2009) A systems approach to evolutionary multi-objective structural optimization and beyond. IEEE Comput Intell Mag 4(3):62–76CrossRef

Jin Y, Wang H, Chugh T, Guo D, Miettinen K (2019) Data-driven evolutionary optimization: an overview and case studies. IEEE Trans Evol Comput 23(3):442–458. https://doi.org/10.1109/TEVC.2018.2869001CrossRef

Juan AA, Faulin J, Grasman SE, Rabe M, Figueira G (2015) A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems. Oper Res Perspect 2:62–72. https://doi.org/10.1016/j.orp.2015.03.001MathSciNetCrossRef

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks (ICNN 1995), pp 1942–1948

Kennedy J, Eberhart R, Shi Y (2001) Swarm intelligence. Morgan Kaufmann Publisher, San Francisco

Kim YS (2011) Multi-objective clustering with data- and human-driven metrics. J Comput Inf Syst 51(4):64–73

Köppen M, Franke K, Vicente-Garcia R (2006) Tiny GAs for image processing applications. IEEE Comput Intell Mag 1(2):17–26. https://doi.org/10.1109/MCI.2006.1626491CrossRef

Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. A Bradford Book

Kulkarni RV, Venayagamoorthy GK (2011) Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(2):262–267CrossRef

LaTorre A, Muelas S, Peña JM (2015) A comprehensive comparison of large scale global optimizers. Inf Sci 316:517–549. https://doi.org/10.1016/j.ins.2014.09.031CrossRef

Lei X, Tian J, Ge L, Zhang A (2013) The clustering model and algorithm of PPI network based on propagating mechanism of artificial bee colony. Inf Sci 247:21–39MathSciNetCrossRef

Lei X, Wang F, Wu FX, Zhang A, Pedrycz W (2016) Protein complex identification through Markov clustering with firefly algorithm on dynamic protein–protein interaction networks. Inf Sci 329:303–316CrossRef

Lei X, Wu FX, Tian J, Zhao J (2014) ABC and IFC: modules detection method for PPI network. BioMed Res Int 2014:1–11. https://doi.org/10.1155/2014/968173CrossRef

Lei X, Yang X, Fujita H (2019) Random walk based method to identify essential proteins by integrating network topology and biological characteristics. Knowl-Based Syst 167:53–67. https://doi.org/10.1016/j.knosys.2019.01.012CrossRef

Li DC, Liu CW (2012) Extending attribute information for small data set classification. IEEE Trans Knowl Data Eng 24(3):452–464. https://doi.org/10.1109/TKDE.2010.254CrossRef

Li S, Kang L, Zhao XM (2014) A survey on evolutionary algorithm based hybrid intelligence in bioinformatics. BioMed Res Int 2014:1–8. https://doi.org/10.1155/2014/362738CrossRef

Lihu A, Holban S (2015) De novo motif prediction using the fireworks algorithm. Int J Swarm Intell Res (IJSIR) 6(3):24–40CrossRef

Liu Y, Ishibuchi H, Yen GG, Nojima Y, Masuyama N (2020) Handling imbalance between convergence and diversity in the decision space in evolutionary multi-modal multi-objective optimization. IEEE Trans Evol Comput 24(3):551–565. https://doi.org/10.1109/TEVC.2019.2938557CrossRef

Lones MA, Tyrrell AM (2005) The evolutionary computation approach to motif discovery in biological sequences. In: Genetic and evolutionary computation conference (GECCO 2005) (pp 1–11). ACM, Washington, DC, USA

Lu J, Li D (2013) Bias correction in a small sample from big data. IEEE Trans Knowl Data Eng 25(11):2658–2663. https://doi.org/10.1109/TKDE.2012.220CrossRef

Lu Y, Wang S, Li S, Zhou C (2011) Particle swarm optimizer for variable weighting in clustering high-dimensional data. Mach Learn 82(1):43–70MathSciNetCrossRef

Lucidi S, Maurici M, Paulon L, Rinaldi F, Roma M (2016) A simulation-based multi-objective optimization approach for health care service management. IEEE Trans Autom Sci Eng 13(4):1480–1491. https://doi.org/10.1109/TASE.2016.2574950CrossRefMATH

Luo W, Yi R, Yang B, Xu P (2019) Surrogate-assisted evolutionary framework for data-driven dynamic optimization. IEEE Trans Emerg Top Comput Intell 3(2):137–150. https://doi.org/10.1109/TETCI.2018.2872029CrossRef

Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivity. Tech. rep., McKinsey Global Institute

Martens D, Baesens B, Fawcett T (2011) Editorial survey: swarm intelligence for data mining. Mach Learn 82(1):1–42MathSciNetCrossRef

Michalewicz Z (2012) Ubiquity symposium: evolutionary computation and the processes of life: the emperor is naked: evolutionary algorithms for real-world applications. Ubiquity 2012:3:1–3:13CrossRef

Morrison RW, De Jong KA (1999) A test problem generator for non-stationary environments. In: Proceedings of the 1999 congress on evolutionary computation (CEC 1999), vol 3, pp 2047–2053

Muñoz MA, Sun Y, Kirley M, Halgamuge SK (2015) Algorithm selection for black-box continuous optimization problems: a survey on methods and challenges. Inf Sci 317:224–245. https://doi.org/10.1016/j.ins.2015.05.010CrossRef

Pal SK, Talwar V, Mitra P (2002) Web mining in soft computing framework: relevance, state of the art and future directions. IEEE Trans Neural Netw 13(5):1163–1177CrossRef

Pan L, He C, Tian Y, Wang H, Zhang X, Jin Y (2019) A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans Evol Comput 23(1):74–88. https://doi.org/10.1109/TEVC.2018.2802784CrossRef

Pati SK, Das AK, Ghosh A (2013) Gene selection using multi-objective genetic algorithm integrating cellular automata and rough set theory. In: Panigrahi BK, Suganthan PN, Das S, Dash SS (eds) Swarm, evolutionary, and memetic computing. Springer International Publishing, Cham, pp 144–155. https://doi.org/10.1007/978-3-319-03756-1_13

Pelikan M, Goldberg DE, Lobo FG (2002) A survey of optimization by building and using probabilistic models. Comput Optim Appl 21(1):5–20MathSciNetMATHCrossRef

Pitiot A, Toga AW, Thompson PM (2002) Adaptive elastic segmentation of brain mri via shape-model-guided evolutionary programming. IEEE Trans Med Imaging 21(8):910–923. https://doi.org/10.1109/TMI.2002.803124CrossRef

Rajaraman A, Leskovec J, Ullman JD (2012) Mining of massive datasets. Cambridge University Press, Cambridge

Rice JR (1976) The algorithm selection problem. Adv Comput 15:65–118. https://doi.org/10.1016/S0065-2458(08)60520-3CrossRef

Riley P (2019) Three pitfalls to avoid in machine learning. Nature 572:27–29. https://doi.org/10.1038/d41586-019-02307-yCrossRef

Saadatmand-Tarzjan M, Moghaddam HA (2007) A novel evolutionary approach for optimizing content-based image indexing algorithms. IEEE Trans Syst Man Cybern Part B (Cybern) 37(1):139–153. https://doi.org/10.1109/TSMCB.2006.880137CrossRef

Saha B, Srivastava D (2014) Data quality: the other face of big data. In: 2014 IEEE 30th international conference on data engineering, pp 1294–1297. https://doi.org/10.1109/ICDE.2014.6816764

Santander-Jiménez S, Vega-Rodríguez MA (2019) Comparative analysis of intra-algorithm parallel multi-objective evolutionary algorithms: Taxonomy implications on bioinformatics scenarios. IEEE Trans Parallel Distrib Syst 30(1):63–78. https://doi.org/10.1109/TPDS.2018.2854788CrossRef

Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised feature selection methods. Pattern Recogn 64:141–158. https://doi.org/10.1016/j.patcog.2016.11.003CrossRefMATH

Sheppard JW, Salzberg SL (1997) A teaching strategy for memory-based control. Artif Intell Rev 11(1–5):343–370CrossRef

Shi Y (2011) An optimization algorithm based on brainstorming process. Int J Swarm Intell Res (IJSIR) 2(4):35–62CrossRef

Shi Y (2018) Unified swarm intelligence algorithms. In: Shi Y (ed) Critical developments and applications of swarm intelligence. IGI Global, pp 1–26

Shin SY, Lee IH, Kim D, Zhang BT (2005) Evolved features for DNA sequence classification and their fitness landscapes. IEEE Trans Evol Comput 9(2):143–158CrossRef

Slowik A, Kwasnicka H (2018) Nature inspired methods and their industry applications—swarm intelligence algorithms. IEEE Trans Ind Inf 14(3):1004–1015. https://doi.org/10.1109/TII.2017.2786782CrossRef

Smith SL, Cagnoni S (2010) Genetic and evolutionary computation: medical applications. Wiley, New YorkCrossRef

Sörensen K (2015) Metaheuristics–the metaphor exposed. Int Trans Oper Res 22(1):3–18MathSciNetMATHCrossRef

Strömbäck L, Freire J (2011) XML management for bioinformatics applications. Comput Sci Eng 13:12–21CrossRef

Sun C, Wang H, Du W, Jin Y (2019) Guest editorial: special issue on computational intelligence in data-driven optimization. IEEE Trans Emerg Top Comput Intell 3(2):90–92. https://doi.org/10.1109/TETCI.2019.2902893CrossRef

Sung WK (2012) Bioinformatics applications in genomics. IEEE Comput 45(6):57–63. https://doi.org/10.1109/MC.2012.151CrossRef

Tan KC, Goh CK (2008) Handling uncertainties in evolutionary multi-objective optimization. In: Zurada JM, Yen GG, Wang J (eds) Computational intelligence: research frontiers. Springer, Berlin, pp 262–292. https://doi.org/10.1007/978-3-540-68860-0_13

Tang K, Peng F, Chen G, Yao X (2014) Population-based algorithm portfolios with automated constituent algorithms selection. Inf Sci 279:94–104. https://doi.org/10.1016/j.ins.2014.03.105CrossRef

Thomas M, Daemen A, De Moor B (2014) Maximum likelihood estimation of GEVD: applications in bioinformatics. IEEE/ACM Trans Comput Biol Bioinf 11(4):673–680CrossRef

Torres A, Nieto JJ (2006) Fuzzy logic in medicine and bioinformatics. J Biomed Biotechnol 2006:1–7. https://doi.org/10.1155/JBB/2006/91908CrossRef

Valentini G, Tagliaferri R, Masulli F (2009) Guest editorial: computational intelligence and machine learning in bioinformatics. Artif Intell Med 45:91–96CrossRef

Verleysen M (2003) Learning high-dimensional data. In: Ablameyko S, Gori M, Goras L, Piuri V (eds) Limitations and future trends in neural computation, NATO Science Series, III: Computer and Systems Sciences, vol 186. IOS Press, pp 141–162

Vlahogianni EI (2015) Optimization of traffic forecasting: intelligent surrogate modeling. Transp Res Part C Emerg Technol 55:14–23. https://doi.org/10.1016/j.trc.2015.03.016CrossRef

Wachowiak MP, Smolíková R, Zheng Y, Zurada JM, Elmaghraby AS (2004) An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans Evol Comput 8(3):289–301. https://doi.org/10.1109/TEVC.2004.826068CrossRef

Wang L, Wang Y, Chang Q (2016) Feature selection methods for big data bioinformatics: a survey from the search perspective. Methods 111:21–31. https://doi.org/10.1016/j.ymeth.2016.08.014CrossRef

Wang Y, Yuan NJ, Sun Y, Qin C, Xie X (2017) App download forecasting: an evolutionary hierarchical competition approach. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI-17), pp 2978–2984. https://doi.org/10.24963/ijcai.2017/415

Xia XG (2017) Small data, mid data, and big data versus algebra, analysis, and topology. IEEE Signal Process Mag 34(1):48–51. https://doi.org/10.1109/MSP.2016.2607319CrossRef

Xu J, Huang E, Chen CH, Lee LH (2015) Simulation optimization: A review and exploration in the new era of cloud computing and big data. Asia-Pac J Oper Res 32(3):1–34. https://doi.org/10.1142/S0217595915500190MathSciNetCrossRefMATH

Xu W, Xu JX, He D, Tan KC (2019) An evolutionary constraint-handling technique for parametric optimization of a cancer immunotherapy model. IEEE Trans Emerg Top Comput Intell 3(2):151–162. https://doi.org/10.1109/TETCI.2018.2880516CrossRef

Yang S, Li C (2010) A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Trans Evol Comput 14(6):959–974CrossRef

Yang Z, Tang K, Yao X (2007) Differential evolution for high-dimensional function optimization. In: Proceedings of 2007 IEEE congress on evolutionary computation (CEC 2007). IEEE, pp 35,231–3530

Yang Z, Tang K, Yao X (2011) Scalability of generalized adaptive differential evolution for large-scale continuous optimization. Soft Comput 15(11):2141–2155CrossRef

Yue C, Qu B, Liang J (2018) A multi-objective particle swarm optimizer using ring topology for solving multi-modal multi-objective problems. IEEE Trans Evol Comput 22(5):805–817. https://doi.org/10.1109/TEVC.2017.2754271CrossRef

Zhang Q, Mhlenbein H (2004) On the convergence of a class of estimation of distribution algorithms. IEEE Trans Evol Comput 8(2):127–136. https://doi.org/10.1109/TEVC.2003.820663CrossRef

Zhang Y, Gong Dw, Cheng J (2017) Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans Comput Biol Bioinf 14(1):64–75. https://doi.org/10.1109/TCBB.2015.2476796CrossRef

Zhang Y, Gong Dw, Sun Jy, Qu By (2018) A decomposition-based archiving approach for multi-objective evolutionary optimization. Inf Sci 430–431:397–413. https://doi.org/10.1016/j.ins.2017.11.052CrossRef

Zhen L, Li M, Peng D, Yao X (2020) Objective reduction for visualising many-objective solution sets. Inf Sci 512:278–294. https://doi.org/10.1016/j.ins.2019.04.014MathSciNetCrossRef

Zhou ZH, Chawla NV, Jin Y, Williams GJ (2014) Big data opportunities and challenges: discussions from data analytics perspectives. IEEE Comput Intell Mag 9(4):62–74CrossRef

Zhu Z, Zhou J, Ji Z, Shi Y (2011) DNA sequence compression using adaptive particle swarm optimization-based memetic algorithm. IEEE Trans Evol Comput 15(5):643–658CrossRef

Title: Evolutionary computation for solving search-based data analytics problems
Authors: Shi Cheng
Lianbo Ma
Hui Lu
Xiujuan Lei
Yuhui Shi
Publication date: 01-08-2020
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 2/2021
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-020-09882-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 2/2021

A survey of sentiment analysis in the Portuguese language

PROMETHEE II method based on variable precision fuzzy rough sets with fuzzy neighborhoods

Chaos Game Optimization: a novel metaheuristic algorithm

Artificial intelligence based on fuzzy logic for the analysis of human movement in healthy people: a systematic review

Bio-inspired VANET routing optimization: an overview

Major advancements in kernel function approximation

Premium Partner