Skip to main content
SpringerLink
Log in

Swarm Programming Using Multi-verse Optimizer

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1392 ))

  • 433 Accesses

Abstract

Swarm programming is the swarm-based automatic programming in which swarm intelligence algorithms are used to evolve computer programs automatically. Automatic programming is a division of machine learning where machines learn how to write the program for themselves. Grammar-based swarm programming is an interesting research topic in recent times. In grammar-based swarm programming, context-free grammar (CFG) is utilized in the generation of computer programs automatically in any arbitrary target computer language. In this paper, grammatical multi-verse optimizer (GMVO) is proposed to generate computer programs automatically. The proposed method is applied to three benchmark problems such as Santa Fe Ant Trail (SFAT), 3-multiplexer, and symbolic regression. The experimental results of the proposed GMVO are compared with that of grammatical fireworks algorithm (GFWA), grammatical bee colony (GBC), and grammatical swarm (GS). The empirical results with analysis demonstrate that the GMVO can be used in automatic generation of computer programs in any arbitrary target computer language.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Rich C, Waters RC (1998) Automatic Programming: Myths and Prospects. IEEE Computer 21(8):40–51

    Article  Google Scholar 

  2. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    Google Scholar 

  3. 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) EuroGP 1998. LNCS, vol 1391. Springer, Heidelberg, pp 83–95

    Google Scholar 

  4. O’Neill M, Ryan C (2001) Grammatical Evolution. IEEE Transactions on Evolutionary Computation 5(4):349–358

    Article  Google Scholar 

  5. Olmo JL, Romero JR, Ventura S (2014) Swarm-based metaheuristics in automatic programming: a survey. WIREs Data Min Knowl Discov https://doi.org/10.1002/widm.1138

  6. Roux O, Fonlupt C (2000) Ant programming: or how to use ants for automatic programming. In: International conference on swarm intelligence (ANTS), pp 121–129

    Google Scholar 

  7. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B Cybern 26:29–41. https://doi.org/10.1109/3477.484436

    Article  Google Scholar 

  8. Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inform Sci 209:1–15. https://doi.org/10.1016/j.ins.2012.05.002

    Article  Google Scholar 

  9. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department

    Google Scholar 

  10. O’Neill M, Brabazon A (2004) Grammatical swarm. In: Genetic and evolutionary computation conference (GECCO), pp 163–174

    Google Scholar 

  11. O’Neill M, Brabazon A (2006) Grammatical Swarm: The Generation of Programs by Social Programming. Natural Computing 5(4):443–462

    Article  MathSciNet  Google Scholar 

  12. O’Neill M, Leahy F, Brabazon A (2006) Grammatical swarm: a variable-length particle swarm algorithm. Swarm intelligent systems. Studies in computational intelligence. Springer, Berlin, pp 59–74

    Google Scholar 

  13. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, Australia

    Google Scholar 

  14. Si T, De A, Bhattacharjee AK (2013) Grammatical bee colony. In: Panigrahi BK et al (eds) SEMCCO 2013, Part I. LNCS, vol 8297, pp 436–445

    Google Scholar 

  15. Si T (2016) Grammatical evolution using fireworks algorithm. In: Pant M et al (eds) Proceedings of fifth international conference on soft computing for problem solving. Advances in intelligent systems and computing, vol 436. https://doi.org/10.1007/978-981-10-0448-3

  16. Tan Y, Zhu Y (2010) Firework algorithm for optimization. In: Tan Y et al (eds) ICSI 2010, Part I. LNCS, vol 6145. Springer, Berlin, pp 355–364

    Google Scholar 

  17. Si T (2021) Swarm programming using moth-flame optimization and whale optimization algorithms. In: Gao XZ, Kumar R, Srivastava S, Soni BP (eds) Applications of artificial intelligence in engineering. Algorithmsfor intelligent systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_3

  18. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2015.07.006

  19. Mirjalili S, Lewis A (2016) The Whale Optimization Algorithm. Advances in Engineering Software 95:51–67

    Article  Google Scholar 

  20. Mahanipour A, Nezamabadi-pour H (2019) GSP: an automatic programming technique with gravitational search algorithm. Appl Intell 49:1502–1516. https://doi.org/10.1007/s10489-018-1327-7

    Article  Google Scholar 

  21. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  Google Scholar 

  22. Togelius J, Nardi RD, Moraglio A (2008) Geometric pso + gp = particle swarm programming. In: 2008 IEEE congress on evolutionary computation (CEC 2008)

    Google Scholar 

  23. Moraglio A, Chio CD, Poli R (2007) Geometric particle swarm optimization. In: Ebner M et al (eds) Proceedings of the European conference on genetic programming (EuroGP). Lecture notes in computer science, vol 4445. Springer, Berlin, pp 125–136

    Google Scholar 

  24. Qing L, Odaka T, Kuroiwa J, Ogura H (2013) Application of an artificial fish swarm algorithm in symbolic regression. IEICE Trans Inf Syst 96:872–885

    Google Scholar 

  25. Husselmann AV, Hawick KA (2014) Geometric firefly algorithms on graphical processing units. Cuckoo search and firefly algorithm. Springer, Berlin, pp 245–269

    Google Scholar 

  26. Headleand C, Teahan W (2013) Grammatical herding. J Comput Sci Syst Biol 6:043–047

    Google Scholar 

  27. Ramstein G, Beaume N, Jacques Y (2008) A grammatical swarm for protein classification. In: IEEE congress on evolutionary computation (IEEE world congress on computational intelligence)

    Google Scholar 

  28. Si T, Sujauddin SK (2016) A comparison of grammatical bee colony and neural networks in medical data mining. Int J Comput Appl (0975 - 8887) 134(6):1–4

    Google Scholar 

  29. Si T, De A, Bhattacharjee AK (2018) Segmentation of brain MRI using wavelet transform and grammatical bee colony. Journal of Circuits, Systems and Computers 27(7):1850108

    Article  Google Scholar 

  30. Tackett WA (1993) Genetic programming for feature discovery and image discrimination. In: ICGA, pp 303–311

    Google Scholar 

  31. Si T, De A, Bhattacharjee AK (2014) Grammatical swarm for artificial neural network training. In: International conference on circuits, power and computing technologies, pp 1657–1661

    Google Scholar 

  32. Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513. https://doi.org/10.1007/s00521-015-1870-7

  33. Derrac J, Garcia S, Molina D, Herrera F (2001) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1:3–18

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, Bankura, West Bengal, India

    Tapas Si

Authors
  1. Tapas Si
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tapas Si .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Aruna Tiwari

  2. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Kapil Ahuja

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Si, T. (2021). Swarm Programming Using Multi-verse Optimizer. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1392 . Springer, Singapore. https://doi.org/10.1007/978-981-16-2709-5_1

Download citation

Publish with us

Policies and ethics

Search

Navigation