Skip to main content

Advertisement

Log in

A framework for designing of genetic operators automatically based on gene expression programming and differential evolution

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

The design of genetic operators is absolutely one of the core work of evolutionary algorithms research. However, the essence of the evolutionary algorithms is that a lot of algorithm design is based on the manual result analysis, summarize, refine, feedback, and then, the algorithms are designed adaptively and correspondingly. This kind of design scheme needs artificial statistics and analysis of large amounts of data, which greatly increases the burden of the designers. To solve this problem, an evolutionary algorithm framework based on genetic operator automatic design is proposed in this paper. In the first step, Gene Expression Programming and Differential Evolution methods are combined together and used to design the genetic operators automatically and adaptively, this hybrid method can not only explore solutions in problem space for the problem solving as most classical evolutionary algorithms do, but also generate genetic operators automatically in operator space for the proper operators extraction and selection related to the evolutionary algorithms . In the second step, the designed operators are adopted into the typical evolutionary algorithms to verify the performance and the result shows that the new designed genetic operator is superior to or at least equivalent to some existing DE variants in a set of classical benchmark functions. More importantly, this paper is not aimed at designing high performance algorithms, but to provide a new perspective for algorithms designing, and to provide a reference scheme for the machine algorithms designing.

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

Similar content being viewed by others

References

  • Antoniouk AV, Khrennikov AY, Kochubei AN (2019) Multidimensional nonlinear pseudo-differential evolution equation with p-adic spatial variables. J Pseudo Differ Oper Appl 2019(50)

  • Arram A, Ayob M (2019) A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems. Comput Ind Eng 133

  • Brest J, Maučec MS (2011) Self-adaptive differential evolution algorithm using population size reduction and three strategies. Soft Comput 15(11):2157–2174

    Article  Google Scholar 

  • Chen F, Shi J, Ma Y, Lei Y, Gong M (2017) Differential evolution algorithm with learning selection strategy for SAR image change detection. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 450–457

  • Contreras-Bolton C, Parada V (2015) Automatic combination of operators in a genetic algorithm to solve the traveling salesman problem. PloS ONE 10(9):e0137724

    Article  Google Scholar 

  • Diosan L, Oltean M (2009) Evolutionary design of evolutionary algorithms. Genetic Program Evolv Mach 10(3):263–306

    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 

  • Goldberg DE (1989) Genetic algorithms in search, Optimization and machine learning, October

  • Hong L, Drake JH, Woodward JR, Özcan E (2018) A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Appl Soft Comput 62:162–175

    Article  Google Scholar 

  • Ibrahim Abdelmonem M, Tawhid Mohamed A (2019) A hybridization of cuckoo search and particle swarm optimization for solving nonlinear systems. Evolut Intell 2019(6)

  • Jiang Dazhi Wu, Kaichao Chen Dicheng, Geng Tu, Teng Zhou, Akhil Garg, Liang Gao (2020) A probability and integrated learning based classification algorithm for high-level human emotion recognition problems. Measurements 150:107049

    Google Scholar 

  • Jiang D, Fan Z (2014) The algorithm for algorithms: an evolutionary algorithm based on automatically designing of genetic operators. Math Probl Eng 2:66–70

    Google Scholar 

  • Jiang Dazhi, Zhijian Wu, Kang Lishan (2006) New method used in gene expression programming: GRCM. J Syst Simul 18:1466–1468

    Google Scholar 

  • Jiang D, Peng C, Fan Z (2014) Evolutionary algorithm based on automatically designing of genetic operators. In: 2013 9th international conference on computational intelligence and security. IEEE, pp 66–70

  • Jiang DT, Geng JD, Kaichao W, Cheng L, Lin Z, Teng Z (2020) A hybrid intelligent model for acute hypotensive episode prediction with large-scale data. Inf Sci

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. IEEE Press, pp 1942–1948

  • Koza JR (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge

    MATH  Google Scholar 

  • Liang J, Wang P, Guo L et al (2019) Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution. Memetic Comput (6)

  • Lin Q, Tang C, Ma Y, Du Z, Li J, Chen J, Ming Z (2017) A novel adaptive control strategy for decomposition-based multiobjective algorithm. Comput Oper Res 78:94–107

    Article  MathSciNet  Google Scholar 

  • Mahanipour A, Nezamabadi-Pour H (2019) GSP: an automatic programming technique with gravitational search algorithm. Appl Intell 49(4):1502–1516

    Article  Google Scholar 

  • Mallipeddi R, Mallipeddi S, Suganthan PN (2010) Ensemble strategies with adaptive evolutionary programming. Inf Sci 180(9):1571–1581

    Article  Google Scholar 

  • Nyathi T, Pillay N (2018) Comparison of a genetic algorithm to grammatical evolution for automated design of genetic programming classification algorithms. Expert Syst Appl 104:213–234

    Article  Google Scholar 

  • Oltean M, Grosan C (2004) Evolving digital circuits using multi expression programming. In: Proceedings. 2004 NASA/DoD conference on evolvable hardware, 2004. IEEE

  • Preen RJ, Smith J (2019) Evolutionary n-level hypergraph partitioning with adaptive coarsening. IEEE Trans Evolut Comput

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417

    Article  Google Scholar 

  • Rodriguez-Coayahuitl L, Morales-Reyes A, Escalante HJ (2019) Evolving autoencoding structures through genetic programming. Genetic Program Evolv Mach 2019(8)

  • Seront G, Bersini H (1996) Simplex GA and hybrid methods. In: Proceedings of IEEE international conference on evolutionary computation, 1996. IEEE

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1(1):67–82

    Article  Google Scholar 

  • Woodward JR, Swan J (2012). The automatic generation of mutation operators for genetic algorithms. In: Proceedings of the 14th annual conference companion on genetic and evolutionary computation. ACM, pp 67–74

  • Xianfang S, Yong Z, Yinan G, Xiaoyan S (2020) Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data. IEEE Trans Evolut Comput. https://doi.org/10.1109/TEVC.2020.2968743

    Article  Google Scholar 

  • Xiao Guorong, Garg Akhil, Chen Dicheng, Jiang Dazhi (2019) AHE detection with a hybrid intelligence model in smart healthcare. IEEE Access 7(1):37360–37370

    Article  Google Scholar 

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958

    Article  Google Scholar 

  • Zhong J, Ong YS, Cai W (2015) Self-learning gene expression programming. IEEE Trans Evolut Comput 20(1):65–80

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank anonymous reviewers for their very detailed and helpful review. This work was supported by National Natural Science Foundation of China (61902232, 61902231), Natural Science Foundation of Guangdong Province (2019A1515010943), Key Project of Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Natural Science) (2018KZDXM035), The Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Special Projects in Artificial Intelligence) (2019KZDZX1030) and 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (2020LKSFG04D).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dazhi Jiang.

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

Jiang, D., Tian, Z., He, Z. et al. A framework for designing of genetic operators automatically based on gene expression programming and differential evolution. Nat Comput 20, 395–411 (2021). https://doi.org/10.1007/s11047-020-09830-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-020-09830-2

Keywords

Navigation