Skip to main content
No Access

An improved multi-expression programming algorithm applied in function discovery and data prediction

Published Online:pp 218-233https://doi.org/10.1504/IJICT.2013.054952

This paper presents an improved multi-expression programming (MEP). In the algorithm, each individual is encoded as a double-layer structure, and two-dimension space operators are introduced through two-dimension crossover and mutation. The problems of symbolic expression are defined and used as benchmarks to compare the effectiveness of proposal method against the baseline single-layer MEP. Experiments showed that our method using two-dimensional super chromosome can find the optimal solution in a short time with small population. Then the improved algorithm is applied to the prediction of 28-day cement compressive strength. Comparison with other three soft computing models, namely MEP model, neural networks (NN) model and fuzzy logic (FL) model on cement strength prediction revealed that the improved MEP model has a lower rate in RMSE and MAE. Test results demonstrate the proposed method is efficient and performed better in function discovery and data prediction.

Keywords

multi-expression programming, MEP, double-layer chromosome, prediction model

References

  • 1. Akkurt, S. , Tayfur, G. , Can, S. (2004). ‘Fuzzy logic model for the prediction of cement compressive strength’. Cement Concrete Research. 34, 8, 1429-1433 Google Scholar
  • 2. Baykasoglu, A. , Güllü, H. , Çanakçi, H. , Özbakir, L. (2008). ‘Prediction of compressive and tensile strength of limestone via genetic programming’. Expert Systems with Applications. 35, 1–2, 111-123 Google Scholar
  • 3. Fa-Liang, G. (1997). ‘A new way of predicting cement strength – fuzzy logic’. Cement Concrete Research. 27, 6, 883-888 Google Scholar
  • 4. Oltean, M. , Dumitrescu, D. (2002). ‘Multi-expressionprogramming’. Cluj-Napoca, Romania:Babes-Bolyai University , Technical-Report, UBB-01-2002 Google Scholar
  • 5. Popovics, S. (1994). ‘History of a mathematical model for strength development of Portland cement concrete’. Materials Journal. 95, 5, 593-600 Google Scholar
  • 6. Sebastia, M. , Olmo, I.F. , Irabien, A. (2003). ‘Neural network prediction of unconfined compressive strength of coal fly ash-cement mixtures’. Cement Concrete Research. 33, 8, 1137-1146 Google Scholar
  • 7. Snell, L.M. , Roekel, J.V. , Wallace, N.D. (1989). ‘Predicting early concrete strength’. International Concrete Research & Information Portal. 11, 12, 43-47 Google Scholar
  • 8. Svinning, K. , Hoskuldsson, A. , Justnes, H. (2008). ‘Prediction of compressive strength up to 28 days from microstructure of Portland cement’. Cement & Concrete Composites. 30, 2, 138-151 Google Scholar
  • 9. Yeh, I-C. (1998). ‘Modeling of strength of high-performance concrete using artificial neural networks’. Cement Concrete Research. 28, 12, 1797-1808 Google Scholar
  • 10. Zhang, Y.M. , Napier-Munn, T.J. (1995). ‘Effects of particle size distribution, surface area and chemical composition on Portland cement strength’. Powder Technol.. 83, 3, 245-252 Google Scholar