Skip to main content
SpringerLink
Log in

Application of Genetic Programming for Electrical Engineering Predictive Modeling: A Review

  • Chapter
Book cover Handbook of Genetic Programming Applications

Abstract

The purpose of having computers automatically resolve problems is essential for machine learning, artificial intelligence and a wide area covered by what Turing called‘machine intelligence’. Genetic programming (GP) is an adaptable and strong evolutionary algorithm with some features that can be very priceless and adequate to get computers automatically to address problems starting from a high-level statement of what to do. Using the concept from natural evolution, GP begins from an ooze of random computer programs and improve them progressively through processes of mutation and sexual recombination until solutions appear. All this without the user needing to know or determine the form or structure of solutions in advance. GP has produced a plethora of human-competitive results and applications, involving novel scientific discoveries and patent-able inventions. The goal of this paper is to give an introduction to the quickly developing field of GP. We begin with a gentle introduction to the basic representation, initialization and operators utilized in GP, completed by a step by step description of their utilization and application. Then, we progress to explain the diversity of alternative representations for programs and more advanced specializations of GP. Despite the fact that this paper has been written with beginners and practitioners in mind, for completeness we also provide an outline of the theoretical aspect available to date for GP.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. (3rd ed.) MIT press, Cambridge, MA

    MATH  Google Scholar 

  • Alavi AH, Ameri M, Gandomi AH, Mirzahosseini MR (2011) Formulation of ow number of asphalt mixes using a hybrid computational method. Constr Build Mater 25(3):1338–1355

    Article  Google Scholar 

  • Sadat Hosseini SS, Gandomi AH (2012) Short-term load forecasting of power systems by gene expression programming. Neural Comput Appl 21(2): 377–389

    Google Scholar 

  • Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems. Neural Comput Appl 21(1):189–201

    Article  Google Scholar 

  • Zhang Q, Fang J, Wang Z, Shi M (2011) Hybrid genetic simulated annealing algorithm with its application in vehicle routing problem with time windows. Adv Mater Res 148–149:395–398

    Article  Google Scholar 

  • Gandomi AH, Yang X-S (2011) Benchmark problems in structural optimization. In: Computational Optimization, Methods and Algorithms. Springer, Berlin/Heidelberg, 259–281

    Google Scholar 

  • Gandomi AH, Alavi AH, Arjmandi P, Aghaeifar A, Seyednour R (2010). Genetic programming and orthogonal least squares: a hybrid approach to modeling the compressive strength of CFRP-confined concrete cylinders.” J Mech Mater Struct 5(5):735–753

    Google Scholar 

  • Javadi A, Rezania M (2009) Applications of artificial intelligence and data mining techniques in soil modeling. Geomech Eng 1(1): 53–74

    Article  Google Scholar 

  • Torres RS, Falcão AX, Gonçalves MA, Papa JP, Zhang B, Fan W, Fox EA (2009) A genetic programming framework for content-based image retrieval. Pattern Recogn 42:283–92

    Article  MATH  Google Scholar 

  • Oltean M, Diosan L (2009) An autonomous GP-based system for regression and classification problems. Appl Soft Comput 9(1):49–60

    Article  Google Scholar 

  • Bezdek JC, Boggavarapu S, Hall LO, Bensaid A (1994) Genetic algorithm guided clustering.In: IEEE World Congress on Computational Intelligence

    Google Scholar 

  • Jie L, Xinbo G, Li-Cheng J (2004) A CSA-based clustering algorithm for large data sets with mixed numeric and categorical values. Fifth World Congress on Intelligent Control and Automation, WCICA, 2303–2307

    Google Scholar 

  • Falco ID, Tarantino E, Cioppa AD, Fontanella F (2006) An innovative approach to genetic programming-based clustering. In: Proc. 9th Online World Conf. Soft Comput. Ind. Appl.(Advances in Soft Computing Series,34)., Berlin, Germany: Springer-Verlag, Sep./Oct, 55–64

    Google Scholar 

  • Liu Y, Ozyer T, Alhajj R, Barker K (2005) Cluster validity analysis of alternative results from multi-objective optimization. In: Proc. 5th SIAM Int Conf Data Mining, Newport Beach, CA, 496–500

    Google Scholar 

  • Alhajj R, Kaya M (2008) Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining. J Intell Inf Syst 31(3): 243–264

    Article  Google Scholar 

  • Lyman M, Lewandowski G (2005) Genetic programming for association rules on card sorting data. In: Proc Genet Evol Comput Conf, Washington, DC: ACM, 1551–1552

    Google Scholar 

  • Yaghouby F, Ayatollahi A, Yaghouby M, Alavi AH (2010) Towards automatic detection of atrial fibrillation: a hybrid computational approach. Comput in Biol Med 40(11–12):919–930

    Article  Google Scholar 

  • Baykasoglu A, Gullub H, Çanakç H, Özbakir L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35(1): 111–123

    Article  Google Scholar 

  • Gandomi A, Alavi A, Sadat Hosseini S (2008) A Discussion on Genetic programming for retrieving missing information in wave records along the west coast of Indian Applied Ocean Research 2007; 29 (3): 99–111.

    Google Scholar 

  • Mwaura J, Keedwell E (2010) Evolution of robotic behaviours using Gene Expression Programming. In: IEEE Congress on Evolutionary Computation (CEC), 1–8

    Google Scholar 

  • Ebner M (1999) Evolving an environment model for robot localization, Euro GP, Ebenhard-Karls-Universitat Tubingen, Germany, Springer Verlag, 184–192

    Google Scholar 

  • Alfaro-Cid E, McGookin EW, Murray-Smith DJ, Fossen TI (2008) Genetic programming for the automatic design of controllers for a surface ship. IEEE Trans Intell Transp Syst 9(2):311–321

    Article  Google Scholar 

  • Dracopoulos DC, Kent S (1997) Genetic programming for prediction and control. Neural Comput Appl 6(4):214–228

    Article  Google Scholar 

  • Nordin P, Banzhaf W (1997) Real time control of a Khepera robot using genetic programmmg. Control Cybern 26(3)

    Google Scholar 

  • Ebner M, Zell (1999) A Evolving a behavior-based control architecture-From simulations to the real world. In: Proceedings of the Genetic and Evolutionary Computation Conference, 1009–1014

    Google Scholar 

  • Suwannik W, Chongstitvatana P (2001) Improving the robustness of evolved robot arm control programs with multiple configurations. In: 2nd Asian Symposium on Industrial Automation and Robotics, Bangkok, Thailand

    Google Scholar 

  • Nordin, Peter, and Wolfgang Banzhaf. “Real time control of a Khepe. ra robot using genetic programmmg.” Control and Cybernetics 26, no. 3 (1997).

    Google Scholar 

  • Grimes CA, (1995) Application of genetic techniques to the planning of railway track maintenance work. in First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, IEE: Sheffield, UK, 414, 467–472

    Google Scholar 

  • Stephenson M, OReilly UM, Martin MC, Amarasinghe S (2003) Genetic programming applied to compiler heuristic optimization, In: Proceedings of the European Conference on Genetic Programming, (Essex, UK), Springer, 238–253

    Google Scholar 

  • Vanneschi L, Cuccu G (2009) A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems. In: Proceedings of the International Conference on Evolutionary Computation, part of the International Joint Conference on Computational Intelligence (IJCCI), ed. by A. Rosa et al

    Google Scholar 

  • Ho, LTW, Ashraf I, Claussen H (2009) Evolving femtocell coverage optimization algorithms using genetic programming. In Personal, Indoor and Mobile Radio Communications, IEEE 20th International Symposium on, 2132–2136

    Google Scholar 

  • Langdon WB, Treleaven P (1997) Scheduling maintenance of electrical power transmission networks using genetic programming. In KevinWarwick, Arthur Ekwue, and Raj Aggarwal, editors, Artificial Intelligence Techniques in Power Systems, chapter 10, 220–237

    Article  Google Scholar 

  • Montana DJ, Czerwinski S (1996) Evolving control laws for a network of traffic signals. In: Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, 333–338. MIT Press, Cambridge

    Google Scholar 

  • Ahmad AM, Khan GM (2012) Bio-signal processing using cartesian genetic programming evolved artificial neural network (cgpann). In: Proceedings of the 10th International Conference on Frontiers of Information Technology, 261–268

    Google Scholar 

  • Holladay K, Robbins K (2007) Evolution of signal processing algorithms using vector based genetic programming. 15th International Conference in Digital Signal Processing, Cardiff, Wales, UK, 503–506

    Google Scholar 

  • Harding S, Leitner J, Schmidhuber J (2013) Cartesian genetic programming for image processing. In Riolo, R., Vladislavleva, E., Ritchie, M. D., and Moore, J. H., editors, Genetic Programming Theory and Practice X, Genetic and Evolutionary Computation, 31–44. Springer New York

    Google Scholar 

  • Sharman KC, Alcazar AIE, Li Y (1995) Evolving signal processing algorithms by genetic programming. First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, IEE, 414, 473–480

    Google Scholar 

  • Esparcia Alcázar AI (1998) Genetic programming for adaptive digital signal processing. PhD thesis, University of Glasgow, Scotland, UK

    Google Scholar 

  • Esparcia-Alcázar A, Sharman K (1999) Genetic Programming for channel equalisation. In R. Poli, H. M. Voigt, S. Cagnoni, D. Corne, G. D. Smith, and T. C. Fogarty, editors, Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, 1596, 126–137, Goteborg, Sweden, Springer-Verlag

    Google Scholar 

  • Alcázar, Anna I. Esparcia, and Ken C. Sharman. “Some applications of genetic programming in digital signal processing.” In Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University, pp. 24–31. 1996

    Google Scholar 

  • Smart W, Zhang M (2003) Classification strategies for image classification in genetic programming. In: Proceeding of Image and Vision Computing Conference, 402–407, New Zealand

    Google Scholar 

  • Li J, Li X, Yao X (2005) Cost-sensitive classification with genetic programming. The IEEE Congress on.Evolutionary Computation

    Google Scholar 

  • Escalante HJ, Acosta-Mendoza N, Morales-Reyes A, Gago-Alonso A (2009) Genetic Programming of Heterogeneous Ensembles for Classification. in Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications, Springer, 9–16

    Google Scholar 

  • Liu KH, Xu CG (2009) A genetic programming-based approach to the classification of multiclass microarray datasets. Bioinformatics 25(3):331–337

    Article  Google Scholar 

  • Zhang L, Nandi AK (2007) Fault classification using genetic programming. Mech Syst Signal Pr 21(3):1273–1284

    Article  Google Scholar 

  • Chaturvedi DK, Mishra RK, Agarwal A (1995) Load Forecasting Using Genetic Algorithms Journal of The Institution of Engineers (India), EL 76, 161–165

    Google Scholar 

  • Dr. Hanan Ahmad Kamal (2002) Solving Curve Fitting problems using Genetic Programming IEEE MELECON May, 7–9

    Google Scholar 

  • Farahat MA (2010) A New Approach for Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms 14th International Middle East Power Systems Conference (MEPCON10)19–21

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, OH, 43606, USA

    Seyyed Soheil Sadat Hosseini & Alireza Nemati

Authors
  1. Seyyed Soheil Sadat Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Nemati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyyed Soheil Sadat Hosseini .

Editor information

Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, USA

    Amir H. Gandomi

  2. Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Michigan, USA

    Amir H. Alavi

  3. Department of Computer Science and Information Systems, University of Limerick, Limerick, Limerick, Ireland

    Conor Ryan

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Hosseini, S.S.S., Nemati, A. (2015). Application of Genetic Programming for Electrical Engineering Predictive Modeling: A Review. In: Gandomi, A., Alavi, A., Ryan, C. (eds) Handbook of Genetic Programming Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20883-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20883-1_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20882-4

  • Online ISBN: 978-3-319-20883-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation