Skip to main content

Advertisement

Log in

Model approach to grammatical evolution: deep-structured analyzing of model and representation

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Grammatical evolution (GE) is a combination of genetic algorithm and context-free grammar, evolving programs for given problems by breeding candidate programs in the context of a grammar using genetic operations. As far as the representation is concerned, classical GE as well as most of its existing variants lacks awareness of both syntax and semantics, therefore having no potential for parallelism of various evaluation methods. To this end, we have proposed a novel approach called model-based grammatical evolution (MGE) in terms of grammar model (a finite state transition system) previously. It is proved, in the present paper, through theoretical analysis and experiments that semantic embedded syntax taking the form of regex (regular expression) over an alphabet of simple cycles and paths provides with potential for parallel evaluation of fitness, thereby making it possible for MGE to have a better performance in coping with more complex problems than most existing GEs.

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

Access this article

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
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Aho AV, Lam MS, Sethi R, Ullman JD (2007) Compilers: principles, techniques, and tools, 2nd edn. Pearson Education Inc., Upper Saddle River

    MATH  Google Scholar 

  • Alfonseca M, Gil FJS (2013) Evolving an ecology of mathematical expressions with grammatical evolution. Biosystems 111(1):111–119

    Article  Google Scholar 

  • Burbidge R, Wilson MS (2014) Vector-valued function estimation by grammatical evolution. Inform Sci 258(1):182–199

    Article  MathSciNet  MATH  Google Scholar 

  • Cano A, Ventura S, Krzysztof JC (2015) Multi-objective genetic programming for feature extraction and data visualization. Soft Comput. doi:10.1007/s00500-015-1907-y

  • Castiglione A, Pizzolante R, De Santis A, Carpentieri B, Castiglione A, Palmieri F (2015) Cloud-based adaptive compression and secure management services for 3D healthcare data. Future Gener Comput Syst 43–44:120–134

  • D’Apiec C, Nicola CD, Manzo V, Moccia R (2014) Optimal scheduling for aircraft departure. J Ambient Intell Hum Comput 5(1):799–807

    Google Scholar 

  • Dempsey I, ONeill M, Brabazon A (2006) Adaptive trading with grammatical evolution. In Proc. of 2006 IEEE Congress on Evolutionary Computation, vol 1, pp 2587–2592

  • Dostal M (2013) Modularity in genetic programming. In: Zelinka I et al (eds) Handbook of optimization. ISRL, pp 38

  • Du X, Ni YC, Xie DT, Yao X, Ye P, Xiao RL (2015) The time complexity analysis of a class of gene expression programming. Soft Comput 19(6):1611–1625

    Article  MATH  Google Scholar 

  • Esposito C, Ficco M, Palmieri F, Castiglione A (2013) Interconnecting federated clouds by using publish-subscribe service. Clust Comput 16(4):887–903

    Article  Google Scholar 

  • Fernandez-Blanco E, Rivero D, Gestal M, Dorado J (2013) Classification of signals by means of genetic programming. Soft Comput 17(1):1929–1937

    Article  Google Scholar 

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MathSciNet  MATH  Google Scholar 

  • Fu W, Johnston M, Zhang M (2015) Genetic programming for edge detection: a gaussian-based approach. Soft Comput 20(3):1231–1248

  • Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416

    Article  MathSciNet  Google Scholar 

  • Harman M, Mansouri SA, Zhang Y (2012) Search-based software engineering: trends, techniques and applications. ACM Comput Surv 45(1):1–61

    Article  Google Scholar 

  • He P, Kang LS, Fu M (2008) Formality based genetic programming. In: IEEE Congress on Evolutionary Computation

  • He P, Kang LS, Johnson CG, Ying S (2011a) Hoare logic-based genetic programming. Sci China Inform Sci 54(3):623–637

  • He P, Johnson CG, Wang HF (2011b) Modeling grammatical evolution by automaton. Sci China Inform Sci 54(12):2544–2553

    Article  MathSciNet  MATH  Google Scholar 

  • He P, Deng ZL, Wang HF, Liu ZS (2015) Model approach to grammatical evolution: theory and case study. Soft Comput. doi:10.1007/s00500-015-1710-9

  • Hopcroft JE, Motwani R, Ullman JD (2008) Automata theory, languages, and computation, 3rd edn. Pearson Education Inc., Upper Saddle River

    MATH  Google Scholar 

  • Hugosson J, Hemberg E, Brabazon A, O’Neill M (2010) Genotype representation in grammatical evolution. Appl Soft Comput 10(1):36–43

    Article  Google Scholar 

  • Kampouridis M, Otero FEB (2015) Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm. Soft Comput. doi:10.1007/s00500-015-1614-8

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

    MATH  Google Scholar 

  • Krawiec K (2014) Genetic programming: where meaning emerges from program code. Genet Progr Evol Mach 15(1):75–77

    Article  Google Scholar 

  • Langdon WB, Harman M (2015) Optimizing existing software with genetic programming. IEEE Trans Evol Comput 19(1):118–135

    Article  Google Scholar 

  • Li J, Wang Q, Wang C, Cao N, Ren K, Lou WJ (2010) Fuzzy keyword search over encrypted data in cloud computing. In: Proceeding of the 29th IEEE International Conference on Computer Communications (INFOCOM 2010), pp 441–445

  • Li J, Huang XY, Li JW, Chen XF, Xiang Y (2014) Securely outsourcing attribute-based encryption with checkability. IEEE Trans Parallel Distrib Syst 25(8):2201–2210

    Article  Google Scholar 

  • Ma T, Zhou JJ, Tang M, Tian Y, AL-Dhelaan A, AL-Rodhaan M, Lee SY (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inform Syst E98–D(4):902–910

    Article  Google Scholar 

  • Mckay RI, Hoai NX, Whigham PA, Shan Y, ONeill M (2010) Grammar-based genetic programming: a survey. Genet Program Evol Mach 11(3/4):365–396

    Article  Google Scholar 

  • Mokryani G, Siano P, Piccolo A (2013) Optimal allocation of wind turbines in microgrids by using genetic algorithm. J Ambient Intell Hum Comput 4(1):613–619

    Article  Google Scholar 

  • Oltean M (2004) Solving even-parity problems using traceless genetic programming. In: IEEE Congress on Evolutionary Computation, vol 2.IEEE, pp 1813–1819

  • Oltean M, Grosan C, Diosan L, Mihaila C (2009) Genetic programming with linear representation: a survey. Int J Artif Intell Tools 19(2):197–239

    Article  Google Scholar 

  • O’Neill M, Ryan C (2001) Grammatical evolution. IEEE Trans Evol Comput 5(4):349–358

    Article  Google Scholar 

  • O’Neill M, Ryan C (2004) Grammatical evolution by grammatical evolution. In: Proceedings Of the 7th European Conference on genetic programming, vol 3003, LNCS, pp 138–149

  • O’Neill M, Brabzaon A, Nicolau M, Mc Garraghy S, Keenan P (2004) \(\pi \) grammatical evolution. In: Proc. GECCO, vol 3103, LNCS, pp 617–629

  • O’Neill M, Vanneschi L, Gustafson S, Banzhaf W (2010) Open issues in genetic programming. Genet Program Evol Mach 11(3):330–363

    Google Scholar 

  • Qian C, Yu Y, Zhou Z-H (2015) Variable solution structure can be helpful in evolutionary optimization. Sci China Inform Sci 58(1):1–17

    Article  MathSciNet  Google Scholar 

  • Risco-Martin JL, Colmenar JM, Hidalgo JI (2014) A methodology to automatically optimize dynamic memory managers applying grammatical evolution. J Syst Softw 91(1):109–123

    Article  Google Scholar 

  • Ryan C, Collins J, O’Neill M (1998) Grammatical evolution: evolving programs for an arbitrary language. In: Proceedings of the first European Workshop on Genetic Programming, vol 1391, LNCS, pp 83–96

  • Wen XZ, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inform Sci 295(1):395–406

    Article  Google Scholar 

  • Wilson D, Kaur D (2009) Search, neutral evolution, and mapping in evolutionary computing: a case study of grammatical evolution. IEEE Trans Evol Comput 13(3):567

    Article  Google Scholar 

  • Xie SD, Wang YX (2014) Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel Pers Commun 78(1):231–246

    Article  Google Scholar 

  • Zheng YH, Jeon BW, Xu DH, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):961–973

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pei He.

Ethics declarations

Funding

This study was funded by National Natural Science Foundation of China (Grant Nos. 61170199, 61302061), the Natural Science Foundation of Guangdong Province, China (Grant No. 2015A030313501), the Scientific Research Fund of Education Department of Hunan Province, China (Grant No. 11A004), Science and Technology Program of Hunan Province, China (Grant No. 2015SK20463), the Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD), Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), and the Open Fund of Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology) (Grant No. kx201208), Construction Project of Innovation Team in Universities in Guangdong Province, China (Grant No. 2015KCXTD014).

Conflicts of interest

Pei He declares that he has no conflict of interest. Zelin Deng declares that he has no conflict of interest. Chongzhi Gao declares that he has no conflict of interest. Xiuni Wang declares that she has no conflict of interest. Jin Li declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, P., Deng, Z., Gao, C. et al. Model approach to grammatical evolution: deep-structured analyzing of model and representation. Soft Comput 21, 5413–5423 (2017). https://doi.org/10.1007/s00500-016-2130-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-016-2130-1

Keywords

Navigation