Skip to main content
SpringerLink
Log in

Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

  • Chapter
  • First Online:
Optimization, Learning, and Control for Interdependent Complex Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1123))

Abstract

A large number of engineering, science, and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. Therefore, data plays a pivotal role in technologies by introducing several challenges: how to present, what to present, why to present. Researchers explored various approaches to implement a comprehensive solution to express their results in every particular domain, such that the solution enhances the performance and minimizes cost, especially time complexity. In this chapter, we focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data, especially large sets of extracted features for a particular purpose. This motivates researchers to think about optimization and apply nature-inspired algorithms, such as meta-heuristic and evolutionary algorithms (EAs) to solve large-scale optimization problems. Building on the strategies of these algorithms, researchers solve large-scale engineering and computational problems with innovative solutions. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. To that end, researchers try to run their algorithms more than usually suggested, around 20 or 30 times, then they compute the mean of result and report only the average of 20/30 runs’ result. This high number of runs becomes necessary because EAs, based on their randomness initialization, converge the best result, which would not be correct if only relying on one specific run. Certainly, researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. P. Marrow, Nature-inspired computing technology and applications. BT Technol. J. 18(4), 13–23 (2000)

    Article  Google Scholar 

  2. M.H. Amini, Distributed computational methods for control and optimization of power distribution networks, PhD Dissertation, Carnegie Mellon University, 2019

    Google Scholar 

  3. M.H. Amini, J. Mohammadi, S. Kar, Distributed holistic framework for smart city infrastructures: tale of interdependent electrified transportation network and power grid. IEEE Access 7, 157535–157554 (2019)

    Article  Google Scholar 

  4. M.H. Amini, J. Mohammadi, S. Kar, Distributed intelligent algorithm for interdependent electrified transportation and power networks, Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (ACM, New York, 2019)

    Google Scholar 

  5. A. Imteaj, M.H. Amini, J. Mohammadi, Leveraging decentralized artificial intelligence to enhance resilience of energy networks. arXiv preprint arXiv:1911.07690 (2019)

    Google Scholar 

  6. M.H. Amini, B. Nabi, M.R. Haghifam, Load management using multi-agent systems in smart distribution network, in 2013 IEEE Power and Energy Society General Meeting (IEEE, New York, 2013)

    Google Scholar 

  7. M.H. Amini, S. Bahrami, F. Kamyab, S. Mishra, R. Jaddivada, K. Boroojeni, P. Weng, Y. Xu, Decomposition methods for distributed optimal power flow: panorama and case studies of the DC model, in Classical and recent aspects of power system optimization (Academic Press, Cambridge, 2018), pp. 137–155

    Google Scholar 

  8. A. Imteaj, M.H. Amini, Distributed sensing using smart end-user devices: pathway to federated learning for autonomous IoT, in 2019 International Conference on Computational Science and Computational Intelligence (Las Vegas, 2019)

    Google Scholar 

  9. F.G. Mohammadi, H.R. Arabnia, ISEA: image steganalysis using evolutionary algorithms. arXiv preprint, arXiv:1907.12914 (2019)

    Google Scholar 

  10. N. Maheswaranathan, L. Metz, G. Tucker, J. Sohl-Dickstein, Guided evolutionary strategies: escaping the curse of dimensionality in random search. arXiv preprint, arXiv:1806.10230 (2018)

    Google Scholar 

  11. M.J.L.F. Cruyff, U. Böckenholt, P.G.M. Van Der Heijden, L.E. Frank, A review of regression procedures for randomized response data, including univariate and multivariate logistic regression, the proportional odds model and item response model, and self-protective responses, in Handbook of Statistics, vol. 34 (Elsevier, Amsterdam, 2016), pp. 287–315

    MATH  Google Scholar 

  12. F. Zhuang, X. Cheng, P. Luo, S.J. Pan, Q. He, Supervised representation learning: transfer learning with deep autoencoders, in Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  13. J. Yang, S. Shebalov, D. Klabjan, Semi-supervised learning for discrete choice models, in IEEE Transactions on Intelligent Transportation Systems (2018)

    Google Scholar 

  14. N. Altman, M. Krzywinski, The curse (s) of dimensionality. Nat. Methods 15, 399–400 (2018)

    Article  Google Scholar 

  15. W. Guo, G. Lynch, J.P. Romano, A new approach for large scale multiple testing with application to FDR control for graphically structured hypotheses. arXiv preprint, arXiv:1812.00258 (2018)

    Google Scholar 

  16. S. Gupta, S. Bhardwaj, P.K. Bhatia, A reminiscent study of nature inspired computation. Int. J. Adv. Eng. Technol. 1(2), 117 (2011)

    Google Scholar 

  17. R. Balamurugan, A.M. Natarajan, K. Premalatha, Stellar-mass black hole optimization for biclustering microarray gene expression data. Appl. Artif. Intell. 29(4), 353–381 (2015)

    Article  Google Scholar 

  18. C. Blum, A. Roli, Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)

    Article  Google Scholar 

  19. S.F. Razavi, H. Sajedi, SVSA: a semi-vortex search algorithm for solving optimization problems. Int. J. Data Sci. Anal. 8, 1–18 (2018)

    Google Scholar 

  20. P. Moscato, C. Cotta, An accelerated introduction to memetic algorithms, in Handbook of Metaheuristics (Springer, Cham, 2019), pp. 275–309

    Google Scholar 

  21. T. Bäck, D.B. Fogel, Z. Michalewicz, Evolutionary Computation 1: Basic Algorithms and Operators (CRC Press, Boca Raton, 2018)

    MATH  Google Scholar 

  22. J. Pierezan, L.D.S. Coelho, Coyote optimization algorithm: a new metaheuristic for global optimization problems, in 2018 IEEE Congress on Evolutionary Computation (CEC) (IEEE, Rio de Janeiro, 2018), pp. 1–8

    Google Scholar 

  23. F.G. Mohammadi, M.S. Abadeh, Image steganalysis using a bee colony based feature selection algorithm. Eng. Appl. Artif. Intell. 31, 35–43 (2014)

    Article  Google Scholar 

  24. F.G. Mohammadi, M.S. Abadeh, A new metaheuristic feature subset selection approach for image steganalysis. J. Intell. Fuzzy Syst. 27(3), 1445–1455 (2014)

    Article  Google Scholar 

  25. D. Wunsch, R. Nigro, G. Coussement, C. Hirsch, Uncertainty quantification in an engineering design software system, in Uncertainty Management for Robust Industrial Design in Aeronautics (Springer, Cham, 2019), pp. 747–754

    Google Scholar 

  26. D.L. Barbour, Precision medicine and the cursed dimensions. npj Digit. Med. 2(1), 4 (2019)

    Google Scholar 

  27. S.N. Karpagam, S. Raghavan, Automated diagnosis system for Alzheimer disease using features selected by artificial bee colony. J. Comput. Theor. Nanosci. 16(2), 682–686 (2019)

    Article  Google Scholar 

  28. W.K. Vong, A.T. Hendrickson, D.J. Navarro, A. Perfors, Do additional features help or hurt category learning? The curse of dimensionality in human learners. Cogn. Sci. 43(3), e12724 (2019)

    Article  Google Scholar 

  29. N.P. Patel, E. Sarraf, M.H. Tsai, The curse of dimensionality. Anesthesiol. J. Am. Soc. Anesthesiol. 129(3), 614–615 (2018)

    Google Scholar 

  30. M. Oudah, A. Henschel, Taxonomy-aware feature engineering for microbiome classification. BMC Bioinf. 19(1), 227 (2018)

    Google Scholar 

  31. A. Serani, M. Diez, J. Wackers, M. Visonneau, F. Stern, Stochastic shape optimization via design-space augmented dimensionality reduction and RANS computations, in AIAA SciTech 2019 Forum (2019), pp. 2218

    Google Scholar 

  32. S.L. Gupta, A.S. Baghel, A. Iqbal, Big data classification using scale-free binary particle swarm optimization, in Harmony Search and Nature Inspired Optimization Algorithms (Springer, Singapore, 2019), pp. 1177–1187

    Book  Google Scholar 

  33. H. Shi, H. Li, D. Zhang, C. Cheng, X. Cao, An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification. Comput. Netw. 132, 81–98 (2018)

    Article  Google Scholar 

  34. U. Khurana, H. Samulowitz, D. Turaga, Feature engineering for predictive modeling using reinforcement learning, in Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  35. D. Zhang, J. Yin, X. Zhu, C. Zhang, Network representation learning: a survey, in IEEE Transactions on Big Data (2018)

    Google Scholar 

  36. R. Vanaja, S. Mukherjee, Novel wrapper-based feature selection for efficient clinical decision support system, in International Conference on Intelligent Information Technologies (Springer, Singapore, 2018), pp. 113–129

    Google Scholar 

  37. E. Hancer, B. Xue, M. Zhang, D. Karaboga, B. Akay, Pareto front feature selection based on artificial bee colony optimization. Inf. Sci. 422, 462–479 (2018)

    Article  Google Scholar 

  38. X.-Y. Liu, Y. Liang, S. Wang, Z.-Y. Yang, H.-S. Ye, A hybrid genetic algorithm with wrapper-embedded approaches for feature selection. IEEE Access 6, 22863–22874 (2018)

    Article  Google Scholar 

  39. V. Rostami, A.S. Khiavi, Particle swarm optimization based feature selection with novel fitness function for image steganalysis, in 2016 Artificial Intelligence and Robotics (IRANOPEN) (IEEE, Qazvin, 2016), pp. 109–114

    Google Scholar 

  40. S. Jiang, S. Yang, A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 21(1), 65–82 (2016)

    Article  Google Scholar 

  41. M.H. Amini, M.P. Moghaddam, O. Karabasoglu, Simultaneous allocation of electric vehicles parking lots and distributed renewable resources in smart power distribution networks. Sustain. Cities Soc. 28, 332–342 (2017)

    Article  Google Scholar 

  42. L.Y. Zhang, G. Luo, L.N. Lu, Genetic algorithms in resource optimization of construction project. J. Tianjin Univ. (Sci. Technol.) 34(2), 188–192 (2001)

    Google Scholar 

  43. M.H. Amini, A. Islam, Allocation of electric vehicles’ parking lots in distribution network, in ISGT 2014 (IEEE, Washington, DC, 2014), pp. 1–5

    Google Scholar 

  44. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1 (MIT Press, Cambridge, 1992)

    MATH  Google Scholar 

  45. I.L.S. Russo, H.S. Bernardino, H.J.C. Barbosa, Knowledge discovery in multiobjective optimization problems in engineering via genetic programming. Expert Syst. Appl. 99, 93–102 (2018)

    Article  Google Scholar 

  46. L.J. Fogel, Artificial intelligence through a simulation of evolution, in Proceedings of the 2nd Cybernetics Science Symposium (1965)

    Google Scholar 

  47. D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga, A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)

    Article  Google Scholar 

  48. D. Karaboga, An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes University, Engineering Faculty, Computer (2005)

    Google Scholar 

  49. F. Zabihi, B. Nasiri, A novel history-driven artificial bee colony algorithm for data clustering. Appl. Soft Comput. 71, 226–241 (2018)

    Article  Google Scholar 

  50. Y. Cao, Y. Lu, X. Pan, N. Sun, An improved global best guided artificial bee colony algorithm for continuous optimization problems. Clust. Comput. 22, 3011–3019 (2018)

    Article  Google Scholar 

  51. Y. Xue, J. Jiang, B. Zhao, T. Ma. A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput. 22, 2935–2952 (2018)

    Article  Google Scholar 

  52. F. Harfouchi, H. Habbi, C. Ozturk, D. Karaboga, Modified multiple search cooperative foraging strategy for improved artificial bee colony optimization with robustness analysis. Soft Comput. 22(19), 6371–6394 (2018)

    Article  Google Scholar 

  53. H. Wang, J.-H. Yi, An improved optimization method based on krill herd and artificial bee colony with information exchange. Memet. Comput. 10(2), 177–198 (2018)

    Article  Google Scholar 

  54. K. Chen, F.-Y. Zhou, X.-F. Yuan, Hybrid particle swarm optimization with spiral-shaped mechanism for feature selection. Expert Syst. Appl. 128, 140–156 (2019)

    Article  Google Scholar 

  55. J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, vol. 4 (IEEE Press, Perth, 1995), pp. 1942–1948

    Google Scholar 

  56. J. Kennedy, Particle swarm optimization, in Encyclopedia of Machine Learning (Springer, Boston, 2010), pp. 760–766

    Google Scholar 

  57. Z.-F. Hao, Z.-G. Wang, H. Huang, A particle swarm optimization algorithm with crossover operator, in 2007 International Conference on Machine Learning and Cybernetics, vol. 2 (IEEE, Hong Kong, 2007), pp. 1036–1040

    Google Scholar 

  58. Y. Zhang, S. Wang, P. Phillips, G. Ji, Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl. Based Syst. 64, 22–31 (2014)

    Article  Google Scholar 

  59. A. Agrawal, S. Tripathi, Particle swarm optimization with probabilistic inertia weight, in Harmony Search and Nature Inspired Optimization Algorithms (Springer, Singapore, 2019), pp. 239–248

    Book  Google Scholar 

  60. M.A. Abido, Optimal design of power-system stabilizers using particle swarm optimization. IEEE Trans. Energy Convers. 17(3), 406–413 (2002)

    Article  Google Scholar 

  61. S. Naka, T. Genji, T. Yura, Y. Fukuyama, Practical distribution state estimation using hybrid particle swarm optimization, in 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No. 01CH37194), vol. 2 (IEEE, Columbus, 2001), pp. 815–820

    Google Scholar 

  62. H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, Y. Nakanishi, A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst. 15(4), 1232–1239 (2000)

    Article  Google Scholar 

  63. M. Dorigo, G.D. Caro, Ant colony optimization: a new meta-heuristic, in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 2 (IEEE, Washington, DC, 1999), pp. 1470–1477

    Google Scholar 

  64. K. Socha, M. Dorigo, Ant colony optimization for continuous domains. Eur. J. Oper. Res. 185(3), 1155–1173 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  65. M.M. Kabir, M. Shahjahan, K. Murase, A new hybrid ant colony optimization algorithm for feature selection. Expert Syst. Appl. 39(3), 3747–3763 (2012)

    Article  Google Scholar 

  66. S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  67. X. Meng, Y. Liu, X. Gao, H. Zhang, A new bio-inspired algorithm: chicken swarm optimization, in International Conference in Swarm Intelligence (Springer, Cham, 2014), pp. 86–94

    Google Scholar 

  68. W. Shi, Y. Guo, S. Yan, Y. Yu, P. Luo, J. Li, Optimizing directional reader antennas deployment in UHF RFID localization system by using a MPCSO algorithm. IEEE Sensors J. 18(12), 5035–5048 (2018)

    Article  Google Scholar 

  69. K. Ahmed, A.E. Hassanien, E. Ezzat, P.-W. Tsai, An adaptive approach for community detection based on chicken swarm optimization algorithm, in International Conference on Genetic and Evolutionary Computing (Springer, Cham, 2016), pp. 281–288

    Google Scholar 

  70. X.-B. Meng, H.-X. Li, Dempster-Shafer based probabilistic fuzzy logic system for wind speed prediction, in 2017 International Conference on Fuzzy Theory and Its Applications (iFUZZY) (IEEE, Pingtung, 2017), pp. 1–5

    Google Scholar 

  71. X.-L. Li, An optimizing method based on autonomous animats: fish-swarm algorithm. Syst. Eng. Theory Pract. 22(11), 32–38 (2002)

    Google Scholar 

  72. Y. Chen, Z. Zeng, J. Lu, Neighborhood rough set reduction with fish swarm algorithm. Soft Comput. 21(23), 6907–6918 (2017)

    Article  Google Scholar 

  73. I. Rahman, J. Mohamad-Saleh, N. Sulaiman, Artificial fish swarm-inspired whale optimization algorithm for solving multimodal benchmark functions, in 10th International Conference on Robotics, Vision, Signal Processing and Power Applications (Springer, Singapore, 2019), pp. 59–65

    Google Scholar 

  74. F.G. Mohammadi, M.H. Amini, Promises of meta-learning for device-free human sensing: learn to sense, in Proceedings of the 1st ACM International Workshop on Device-Free Human Sensing (DFHS’19) (ACM, New York, 2019), pp. 44–47. https://doi.org/10.1145/3360773.3360884

    Google Scholar 

  75. F.G. Mohammadi, M.H. Amini, H.R. Arabnia, Applications of nature-inspired algorithms for dimension reduction: enabling efficient data analytics. arXiv preprint, arXiv: 1908.08563 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, USA

    Farid Ghareh Mohammadi & Hamid R. Arabnia

  2. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    M. Hadi Amini

  3. Sustainability, Optimization, and Learning for InterDependent Networks Laboratory (solid lab), Florida International University, Miami, FL, USA

    M. Hadi Amini

Authors
  1. Farid Ghareh Mohammadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Hadi Amini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid R. Arabnia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Hadi Amini .

Editor information

Editors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, Miami, FL, USA, Sustainability, Optimization, and Learning for InterDependent Networks Laboratory (solid lab), Florida International University, Miami, FL, USA

    M. Hadi Amini

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mohammadi, F.G., Amini, M.H., Arabnia, H.R. (2020). Evolutionary Computation, Optimization, and Learning Algorithms for Data Science. In: Amini, M. (eds) Optimization, Learning, and Control for Interdependent Complex Networks. Advances in Intelligent Systems and Computing, vol 1123. Springer, Cham. https://doi.org/10.1007/978-3-030-34094-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34094-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34093-3

  • Online ISBN: 978-3-030-34094-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation