Skip to main content
Log in

Discussion on “Alternative data-driven methods to estimate wind from waves by inverse modeling” by Mansi Daga, M. C. Deo [Natural Hazards (2008) NHAZ 524, Article 9299, DOI 10.1007/s11069-008-9299-2]

  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The paper studied by Daga and Deo (2008) considers the feasibility of using genetic programming (GP) for estimating wind from waves by inverse modeling. The paper includes some problems about the fundamental aspects and use of the proposed GP approach for the aim of their study. In this discussion, some controversial points of the paper are given.

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

References

  • Alavi AH, Heshmati AAR, Gandomi AH, Askarinejad A, Mirjalili M (2008) Utilisation of computational intelligence techniques for stabilised soil. In: Papadrakakis M, Topping BHV (eds) Proceedings 6th international conference on engineering computational technology. Civil-Comp Press, Edinburgh, Scotland, Paper 175

  • Banzhaf W, Nordin P, Keller R, Francone FD (1998) Genetic programming—an introduction: on the automatic evolution of computer programs and its application. dpunkt/Morgan Kaufmann, Heidelberg/San Francisco

  • Brameier M, Banzhaf W (2001) Evolving teams of predictors with linear genetic programming. Genet Program Evolvable Mach 2(4):381–407. doi:10.1023/A:1012978805372

    Article  Google Scholar 

  • Brameier M, Banzhaf W (2007) Linear genetic programming. Springer, New York

    Google Scholar 

  • Brameier M, Kantschik W, Dittrich P, Banzhaf W (1998) SYSGP—A C++ library of different GP variants. Technical Report [CI-98/48]. Collaborative Research Center 531, University of Dortmund, Germany

  • Conrads M, Dolezal O, Francone FD, Nordin P (1998) Discipulus—fast genetic programming based on AIM learning technology. Register Machine Learning Technologies, Littleton, CO

    Google Scholar 

  • Deschaine LM, Francone FD (2002) Comparison of Discipulus™ linear genetic programming software with support vector machines, classification trees, neural networks and human experts. Register Machine Learning Technologies Inc., Littleton, CO. www.rmltech.com/Comparison.White.Paper.pdf. Accessed 25 Jan 2009. (white paper)

  • Francone FD, Deschaine LM (2004) Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based linear genetic programming. Inf Sci J 161(3–4):99–120. doi:10.1016/j.ins.2003.05.006

    Article  Google Scholar 

  • Francone FD, Deschaine LD, Battenhouse T, Warren JJ (2005) Discrimination of unexploded ordnance from clutter using linear genetic programming, Chap 4. In: Yu T, Riolo RL, Worzel B (eds) Genetic programming theory and practice III. Springer, New York

  • Gandomi AH, Alavi AH, Kazemi S, Alinia MM (2008) Behavior appraisal of steel semi-rigid joints using linear genetic programming. J Constr Steel Res (in press)

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

    Google Scholar 

  • Langdon WB, Banzhaf W (2005) Repeated sequences in linear genetic programming genomes. Complex Syst 15(4):285–306

    Google Scholar 

  • Nordin PJ (1994) A compiling genetic programming system that directly manipulates the machine code. In: Kenneth E, Kinnear J (eds) International conference on advances in genetic programming. MIT Press, USA, pp 311–331

    Google Scholar 

  • Poli R, Langdon WB, McPhee NF, Koza JR (2007) Genetic programming: an introductory tutorial and a survey of techniques and applications. Technical Report [CES-475]. University of Essex, UK

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. H. Gandomi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gandomi, A.H., Alavi, A.H. & Taghipour, A. Discussion on “Alternative data-driven methods to estimate wind from waves by inverse modeling” by Mansi Daga, M. C. Deo [Natural Hazards (2008) NHAZ 524, Article 9299, DOI 10.1007/s11069-008-9299-2]. Nat Hazards 52, 671–673 (2010). https://doi.org/10.1007/s11069-009-9400-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-009-9400-5

Keywords

Navigation