Model approach to grammatical evolution: deep-structured analyzing of model and representation
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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.
KeywordsGenetic programming Grammatical evolution Finite state automaton Model
Compliance with ethical standards
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.
This article does not contain any studies with human participants or animals performed by any of the authors.
- 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 43–44:120–134Google Scholar
- D’Apiec C, Nicola CD, Manzo V, Moccia R (2014) Optimal scheduling for aircraft departure. J Ambient Intell Hum Comput 5(1):799–807Google 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 2587–2592Google Scholar
- Dostal M (2013) Modularity in genetic programming. In: Zelinka I et al (eds) Handbook of optimization. ISRL, pp 38Google Scholar
- Fu W, Johnston M, Zhang M (2015) Genetic programming for edge detection: a gaussian-based approach. Soft Comput 20(3):1231–1248Google Scholar
- He P, Kang LS, Fu M (2008) Formality based genetic programming. In: IEEE Congress on Evolutionary ComputationGoogle Scholar
- He P, Kang LS, Johnson CG, Ying S (2011a) Hoare logic-based genetic programming. Sci China Inform Sci 54(3):623–637Google 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
- 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
- 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 441–445Google Scholar
- Oltean M (2004) Solving even-parity problems using traceless genetic programming. In: IEEE Congress on Evolutionary Computation, vol 2.IEEE, pp 1813–1819Google 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 138–149Google Scholar
- O’Neill M, Brabzaon A, Nicolau M, Mc Garraghy S, Keenan P (2004) \(\pi \) grammatical evolution. In: Proc. GECCO, vol 3103, LNCS, pp 617–629Google Scholar
- O’Neill M, Vanneschi L, Gustafson S, Banzhaf W (2010) Open issues in genetic programming. Genet Program Evol Mach 11(3):330–363Google 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 83–96Google 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):961–973Google Scholar