Skip to main content
Springer Nature Link
Log in

Evolution of biocoenosis through symbiosis with fitness approximation for many-tasking optimization

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

Memetic computing is a blooming research area, which treats memes as the fundamental building blocks of information transfer. Evolutionary multitasking is an emerging topic in memetic computation, which applies evolutionary algorithm to optimize multiple tasks at a time. A famous class of algorithms for evolutionary multitasking is the multi-factorial evolutionary algorithm (MFEA). Nevertheless, current MFEAs only consider problems with small number of tasks, resulting in a lack of effective information transfer strategy. This study proposes a framework for evolutionary multitasking, called the evolution of biocoenosis through symbiosis with fitness approximation (EBSFA). The EBSFA incorporates evolution of biocoenosis through symbiosis (EBS) with fitness approximation to ameliorate the information transfer. The improvement of EBSFA is three-fold, including (1) the adaptive control of information transfer among tasks, (2) the selection of individuals from the universal offspring pool for evaluation based on fitness approximation, and (3) an ensemble method for improving the accuracy of fitness approximation through k nearest neighbors. Experimental analysis verifies the effectiveness and efficiency of the proposed EBSFA, by comparison with an advanced single-tasking method, the covariance matrix adaptation evolution strategy (CMAES), an illustrious multitasking optimization method, the MFEA-II, and an evolutionary many-tasking method, the EBS on a set of many-tasking benchmark problems. The results show that EBSFA can gain nice solution quality and fast convergence speed. Further analysis validates the effectiveness of the proposed components on improving the information transfer.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bali KK, Ong YS, Gupta A, Tan PS (2019) Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II. IEEE Trans Evol Comput 24(1):69–83

    Article  Google Scholar 

  2. Bali KK, Ong YS, Gupta A, Tan PS (2020) Cognizant multitasking in multiobjective multifactorial evolution: MO-MFEA-II. IEEE Trans Cybern, p 13

  3. Broomhead D, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. In: RSRE memorandum/royal signals and radar establishment, vol 4148. Royals Signals and Radar Establishment

  4. Carpenter W, Barthelemy J (1993) A comparison of polynomial approximations and artificial neural nets as response surfaces. Struct Optim 5:166–174

    Article  Google Scholar 

  5. Chandra R, Gupta A, Ong YS, Goh CK (2016) Evolutionary multi-task learning for modular training of feedforward neural networks. In: Proceedings of international conference on neural information processing

  6. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41

    Article  Google Scholar 

  7. Ebrahimi M, Farmani MR, Roshanian J (2011) Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization. Struct Multidiscip Optim 44(6):773–784

    Article  Google Scholar 

  8. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Natural computing. Springer, Berlin

    Book  Google Scholar 

  9. Fonseca LG, Lemonge AC, Barbosa HJ (2012) A study on fitness inheritance for enhanced efficiency in real-coded genetic algorithms. In: Proceedings of IEEE congress on evolutionary computation, pp 1–8

  10. Goldberg DE (1989) Genetic algorithms in search. Optimization and machine learning. Addison Wesley, Reading

    MATH  Google Scholar 

  11. Gong W, Zhou A, Cai Z (2015) A multioperator search strategy based on cheap surrogate models for evolutionary optimization. IEEE Trans Evol Comput 19(5):746–758

    Article  Google Scholar 

  12. Gupta A, Mańdziuk J, Ong YS (2015) Evolutionary multitasking in bi-level optimization. Complex Intell Syst 1(1):83–95

    Article  Google Scholar 

  13. Gupta A, Da B, Yuan Y, Ong YS (2016) On the emerging notion of evolutionary multitasking: a computational analog of cognitive multitasking, Adaptation and learning and optimization, vol 20. Springer, Berlin

    Google Scholar 

  14. Gupta A, Ong YS, Da B, Feng L, Handoko SD (2016) Landscape synergy in evolutionary multitasking. In: Proceedings of IEEE congress on evolutionary computation

  15. Gupta A, Ong YS, Feng L (2016) Multifactorial evolution: toward evolutionary multitasking. IEEE Trans Evol Comput 20(3):343–357

    Article  Google Scholar 

  16. Hansen N (2006) The CMA evolution strategy: a comparing review. In: Inza I, Bengoetxea E, Lozano JA, Nga PL (eds) Towards a new evolutionary computation, Advances in estimation of distribution algorithms. Springer, Berlin, pp 75–102

    Chapter  Google Scholar 

  17. Hashimoto R, Ishibuchi H, Masuyama N, Nojima Y (2018) Analysis of evolutionary multi-tasking as an island model. In: Proceedings of the genetic and evolutionary computation conference companion, pp 1894–1897

  18. Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  19. Jin Y (2011) Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol Comput 1:61–70

    Article  Google Scholar 

  20. Lesh FH (1959) Multi-dimensional least-square polynomial curve fitting. Commun ACM 2(9):29–30

    Article  Google Scholar 

  21. Liang JJ, Qu BY, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session and competition on real-parameter optimization. Nanyang Technological University, Technical report

  22. Liaw RT, Ting CK (2016) Enhancing covariance matrix adaptation evolution strategy through fitness inheritance. In: Proceedings of the IEE congress on evolutionary computation, pp 1956–1963

  23. Liaw RT, Ting CK (2017) Evolutionary many-tasking based on biocoenosis through symbiosis: a framework and benchmark problems. In: Proceedings of IEEE congress on evolutionary computation, pp 2266–2273

  24. Liaw RT, Ting CK (2019) Evolutionary manytasking optimization based on symbiosis in biocoenosis. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 4295–4303

  25. Liu B, Chen Q, Zhang Q, Gielen G, Grout V (2014) Behavioral study of the surrogate model-aware evolutionary search framework. In: Proceedings of IEEE congress on evolutionary computation, pp 715–722

  26. Luong HN, Nguyen HTT, Ahn CW (2012) Entropy-based efficiency enhancement techniques for evolutionary algorithms. Inf Sci 188:100–120

    Article  Google Scholar 

  27. Myers R, Montgomery D (1995) Response surface methodology. Wiley, New York

    MATH  Google Scholar 

  28. Ong YS, Zhou Z, Lim D (2006) Curse and blessing of uncertainty in evolutionary algorithm using approximation. In: Proceedings of IEEE congress on evolutionary computation, pp 2928–2935

  29. Pelikan M, Sastry K (2004) Fitness inheritance in the Bayesian optimization algorithm. In: Proceedings of genetic and evolutionary computation conference, pp 48–59

  30. Pilato C, Tumeo A, Palermo G, Ferrandi F, Lanzi PL, Sciuto D (2008) Improving evolutionary exploration to area-time optimization of FPGA designs. J Syst Archit 54(11):1046–1057

    Article  Google Scholar 

  31. Pilato C, Loiacono D, Tumeo A, Ferrandi F, Lanzi PL, Sciuto D (2010) Speeding-up expensive evaluations in high-level synthesis using solution modeling and fitness inheritance. In: Proceedings of computational intelligence in expensive optimization problems, pp 701–723

  32. Rechenberg I, Brand F, Hansen N, Herdy M, Ostermeier A (1995) Theorie der evolutionsstrategie–von der zufallssuche zur intelligenten strategie. Tagungsband zum Statusseminar des BMBF Bioinformatik G. Wolf and R. Schmidt and M. van der Meer

  33. Regis RG (2014) Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions. IEEE Trans Evol Comput 18(3):326–347

    Article  Google Scholar 

  34. Sacks J, Welch WJ, Michell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4:409–435

    Article  MathSciNet  Google Scholar 

  35. Sagarna R, Ong YS (2016) Concurrently searching branches in software tests generation through multitask evolution. In: Proceedings of IEEE symposium series on computational intelligence

  36. Sastry K, Goldberg DE, Pelikan M (2001) Don’t evaluate, inherit. In: Proceedings of the genetic and evolutionary computation conference, pp 551–558

  37. Sastry K, Goldberg DE, Pelikan M (2004) Efficiency enhancement of probabilistic model building genetic algorithms. Report 200420, IlliGAL

  38. Sastry K, Pelikan M, Goldberg DE (2004) Efficiency enhancement of genetic algorithms via building-block-wise fitness estimation. In: Proceedings of IEEE congress on evolutionary computation, vol 1, pp 720–727

  39. Sastry K, Lima CF, Goldberg DE (2006) Evaluation relaxation using substructural information and linear estimation. In: Proceedings of the genetic and evolutionary computation conference, pp 419–426

  40. Shyy W, Tucker PK, Vaidyanathan R (2001) Response surface and neural network techniques for rocket engine injector optimization. J Propul Power 17(2):391–401

    Article  Google Scholar 

  41. Smith RE, Dike BA, Stegmann SA (1995) Fitness inheritance in genetic algorithms. In: Proceedings of the ACM symposium on applied computing, pp 345–350

  42. Strasser S, Sheppard J, Fortier N, Goodman R (2016) Factored evolutionary algorithms. IEEE Trans Evol Comput 21:281–293

    Article  Google Scholar 

  43. Vapnik V (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  44. Wang TC, Liaw RT (2020) Multifactorial genetic fuzzy data mining for building membership functions. In: Proceedings of IEEE congress on evolutionary computation

  45. Wen YW, Ting CK (2017) Parting ways and reallocating resources in evolutionary multitasking. In: Proceedings of IEEE congress on evolutionary computation, pp 2404–2411

  46. Williams CKI, Rasmussen CE (1996) Gaussian processes for regression. In: Touretsky DS, Mozer MC, Hasselmo ME (eds) Advances in neural information processing systems, vol 8. MIT Press, Cambridge, pp 514–520

    Google Scholar 

  47. Zhou L, Feng L, Zhong J, Ong YS, Zhu Z, Sha E (2016) Evolutionary multitasking in combinatorial search spaces: a case study in capacitated vehicle routing problem. In: Proceedings of IEEE symposium series on computational intelligence

Download references

Acknowledgements

This work was supported by the Ministry of Science and Technology of Taiwan, under contracts MOST 109-2218-E-030-001-MY3, MOST 109-2634-F-007-020, and MOST 108-2634-F-007-012.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City, Taiwan

    Rung-Tzuo Liaw

  2. Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan

    Chuan-Kang Ting

Authors
  1. Rung-Tzuo Liaw
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chuan-Kang Ting
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Rung-Tzuo Liaw or Chuan-Kang Ting.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liaw, RT., Ting, CK. Evolution of biocoenosis through symbiosis with fitness approximation for many-tasking optimization. Memetic Comp. 12, 399–417 (2020). https://doi.org/10.1007/s12293-020-00317-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-020-00317-2

Keywords