Encyclopedia of Complexity and Systems Science

Living Edition
| Editors: Robert A. Meyers

Advanced Evolutionary Algorithms in Data Mining

  • Janez BrestEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27737-5_650-1

Definition of the Subject

Evolutionary algorithms (EAs) are stochastic population-based methods inspired by nature. A population consists of several individuals usually encoded as vectors. During an evolutionary process, a population is transformed into a new population. After some such transformations, the algorithm stops and returns a best found individual as solution.

Differential Evolution (DE) is an evolutionary algorithm for global optimization over continuous spaces as well as for optimization over discrete spaces. Nowadays, it is used as a powerful global optimization method within a wide range of research areas.

As a plethora of data are generated in every possible means and data dimensionality increases on a large scale, it is imperative to increase power of methods in data mining, knowledge discovery, as well as in optimization methods that are dealing with high-dimensional massive data, uncertainty environments, and dynamic systems.


(Das and Suganthan 2011): To...


Evolutionary Algorithm Differential Evolution Memetic Algorithm Differential Evolution Algorithm Artificial Immune System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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  1. Bäck T, Fogel DB, Michalewicz Z (eds) (1997) Handbook of evolutionary computation, IOP Publishing Ltd., Bristol, UKGoogle Scholar
  2. Bošković B, Brest J, Zamuda A, Greiner S, Žumer V (2011) History mechanism supported differential evolution for chess evaluation function tuning. Soft Comput Fusion Found Methodol Appl 15:667–682Google Scholar
  3. Brest J, Maučec MS (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247CrossRefGoogle Scholar
  4. Brest J, Greiner S, Bošković B, Mernik M, Žumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657CrossRefGoogle Scholar
  5. Brest J, Korošec P, Šilc J, Zamuda A, Bošković B, Maučec MS (2013) Differential evolution and differential ant-stigmergy on dynamic optimisation problems. Int J Syst Sci 44:663–679zbMATHCrossRefGoogle Scholar
  6. Brest J, Zamuda A, Bošković B (2015) Adaptation in the differential evolution. In: Fister I, Fister I Jr (eds) Adaptation and hybridization in computational intelligence. Adaptation, learning, and optimization, vol 18. Springer International Publishing, Cham, CH. pp 53–68Google Scholar
  7. Cheng J, Zhang G, Neri F (2013) Enhancing distributed differential evolution with multicultural migration for global numerical optimization. Inform Sci 247:72–93MathSciNetCrossRefGoogle Scholar
  8. Das S, Suganthan P (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):27–54Google Scholar
  9. Das S, Abraham A, Chakraborty U, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553CrossRefGoogle Scholar
  10. Eiben AE, Smith JE (2003) Introduction to evolutionary computing, Natural computing. Springer, BerlinzbMATHCrossRefGoogle Scholar
  11. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141CrossRefGoogle Scholar
  12. Elsayed S, Sarker R, Essam D (2014) A self-adaptive combined strategies algorithm for constrained optimization using differential evolution. Appl Math Comput 241:267–282MathSciNetCrossRefGoogle Scholar
  13. Feoktistov V (2006) Differential evolution: in search of solutions (Springer optimization and its applications). Springer, New York/SecaucusGoogle Scholar
  14. Glotić A, Zamuda A (2015) Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution. Appl Energy 141:42–56CrossRefGoogle Scholar
  15. Karafotias G, Hoogendoorn M, Eiben A (2015) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187CrossRefGoogle Scholar
  16. Liu J, Lampinen J (2005) A fuzzy adaptive differential evolution algorithm. Soft Comput 9(6):448–462zbMATHCrossRefGoogle Scholar
  17. Mallipeddi R, Suganthan P (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14(4):561–579CrossRefGoogle Scholar
  18. Mukhopadhyay A, Maulik U, Bandyopadhyay S, Coello C (2014a) Survey of multiobjective evolutionary algorithms for data mining: part II. IEEE Trans Evol Comput 18(1):20–35CrossRefGoogle Scholar
  19. Mukhopadhyay A, Maulik U, Bandyopadhyay S, Coello Coello C (2014b) A survey of multiobjective evolutionary algorithms for data mining: part I. IEEE Trans Evol Comput 18(1):4–19CrossRefGoogle Scholar
  20. Neri F, Tirronen V (2009) Scale factor local search in differential evolution. Memetic Comp 1(2):153–171CrossRefGoogle Scholar
  21. Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1–2):61–106CrossRefGoogle Scholar
  22. Price KV, Storn RM, Lampinen JA (2005) Differential evolution, a practical approach to global optimization. Springer, Berlin Heidelberg, Germany.Google Scholar
  23. Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417CrossRefGoogle Scholar
  24. Storn R, Price K (1995) Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, BerkeleyGoogle Scholar
  25. Storn R, Price K (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359zbMATHMathSciNetCrossRefGoogle Scholar
  26. Tanabe R, Fukunaga A (2013) Evaluating the performance of shade on cec 2013 benchmark problems. In: 2013 IEEE congress on evolutionary computation (CEC), 20–23 june, 2013, Cancun, Mexico. IEEE, pp 1952–1959Google Scholar
  27. Tanabe R, Fukunaga A (2014) Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC2014), 6–11 July 2014, Beijing, China. IEEE, pp 1658–1665Google Scholar
  28. Teng NS, Teo J, Hijazi MHA (2009) Self-adaptive population sizing for a tune-free differential evolution. Soft Comput Fusion Found Methodol Appl 13(7):709–724Google Scholar
  29. Wang H, Rahnamayan S, Wu Z (2013) Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems. J Parallel Distrib Comput 73(1):62–73CrossRefGoogle Scholar
  30. Wu X, Zhu X, Wu G-Q, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107CrossRefGoogle Scholar
  31. Zamuda A, Brest J (2014) Vectorized procedural models for animated trees reconstruction using differential evolution. Inform Sci 278:1–21MathSciNetCrossRefGoogle Scholar
  32. Zamuda A, Sosa JDH (2014) Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures. Appl Soft Comput 24:95–108CrossRefGoogle Scholar
  33. Zamuda A, Brest J, Bošković B, Žumer V (2011) Differential evolution for parameterized procedural woody plant models reconstruction. Appl Soft Comput 11:4904–4912CrossRefGoogle Scholar
  34. Zhang J, Sanderson A (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering and Computer Science, Institute of Computer ScienceUniversity of MariborMariborSlovenia