Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Novel Multi-Gen Multi Parameter Genetic Algorithm Representation for Attributes Selection and Porosity Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Muna Hadi Saleh, Hadeel Mohammed Tuama

Muna Hadi Saleh and Hadeel Mohammed Tuama. Novel Multi-Gen Multi Parameter Genetic Algorithm Representation for Attributes Selection and Porosity Prediction. International Journal of Computer Applications 141(4):34-39, May 2016. BibTeX

	author = {Muna Hadi Saleh and Hadeel Mohammed Tuama},
	title = {Novel Multi-Gen Multi Parameter Genetic Algorithm Representation for Attributes Selection and Porosity Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {4},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {34-39},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016909614},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Many applications require a careful selection of attributes or features from a much larger set of data. This attributes selection problem need to optimized. In order to tackle this problem this paper proposes a binary-real code multi-gen multi-parameter genetic algorithm for attributes selection from large seismic data and prediction of effective porosity. Genetic Algorithm (GA) uses three selection methods for this purpose, mean square error and correlation coefficient are two witness criteria to choose the best subset of attributes that minimize the error and give high prediction of porosity.


  1. D. Subrahmanyam, P.H. Rao, 2008. Seismic Attributes- A Review, International Conference and Exposition on Petroleum Geophysics, P. 398.
  2. Satinder Chopra and Kurt Marfurt, 2006. Seismic Attributes – a Atomising Aid for Geologic Prediction, Allied Geophysical Laboratories, University of Houston, Houston, Texas, USA.
  3. Al-Qahtani, F. A. 2000. Porosity Distribution Prediction Using Artificial Neural Networks. College of Engineering and Mining Recourses, West Virginia University, M.Sc. Thesis, 1-3.
  4. Asoodeh, M., Bagheripour, P. 2012. Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems. Journal of Rock Mechanics and Rock Engineering 45, 45-63.
  5. F. HERRERA, M. LOZANO and J.L. 1998. VERDEGAY, Tackling Real-Coded Genetic Algorithms, Artificial Intelligence Review 12: 265–319.
  6. Adi A. Maaita, Jamal Zraqou, Fadi Hamad and Hamza A. Al-Sewadi, 2015. A Generic Adaptive Multi-Gene-Set Genetic Algorithm (AMGA), International Journal of Advanced Computer Science and Applications", Vol. 6, No. 5.
  7. Kalyanmoy Deb, Amarendra Kumar, 1995. Real-coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems, Complex Systems 9, 431-454.
  8. Jihoon Yang and Vasant Honavar, 1997. Feature Subset Selection Using a Genetic Algorithm. May, 3
  9. Kevin P. Dorrington and Curtis A. Link, 2004. Genetic-algorithm/neural-network approach to seismic
  10. attribute selection for well-log prediction", Geophysics, vol. 69, No. 1 (january-feburaty), P. 212–221, 14 FIGS., 2 TABLES.
  11. Natalia Soubotcheva and Robert R. Stewart, (2004). Predicting porosity logs from seismic attributes using geostatistics, Vol. 16.
  12. Barani R. and Sumathi M., A 2013. Multi-gene, Multi-parameter Genetic Algorithm for Block-Based Feature-Level Image Fusion", CSIP 2013, pp. 188–201. Elsevier Publications.
  13. Ursula Iturrarán-Viveros, Jorge O. Parra, 2014. Artificial Neural Networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well log data, Journal of Applied Geophysics 107, 45–54.
  14. Randy L. Haupt and Sue Ellen Haupt, 2004. PRACTICAL GENETIC ALGORITHMS, Wiley.
  15. Antonisse, J. 1989. A New Interpretation of Schema Notation that Overturns the Binary Encoding Constraint. Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.), (Morgan Kaufmann Publishers, San Mateo), 86–91.
  16. David Shen, WCI, Inc., 2016. Computation of Correlation Coefficient and Its Confidence Interval in SAS, Paper 170-31.
  17. T. Chai and R. R. Draxler," Root mean square error (RMSE) or mean absolute error (MAE)? –Arguments against avoiding RMSE in the literature", Geosci. Model Dev., 7, 1247–1250, 2014.
  18. Daniel P. Hampson, James S. Schuelke, and John A. Quirein," Use of multiattribute transforms to predict log properties from seismic data", GEOPHYSICS, VOL. 66, NO. 1, P. 220–236, JANUARY-FEBRUARY 2001.
  19. Noraini Mohd Razali, John Geraghty, 2011. Genetic Algorithm Performance with Different Selection Strategies in Solving TSP", Vol II.
  20. Riikka Peltokangas and Aki Sorsa, 2008. Real-Coded Genetic Algorithms and Nonlinear Parameter identification, No. 34, April.
  21. Arumugan M.S. and Rao M.V.C. 2004. Novel Hybrid Approaches for Real Coded Genetic Algorithm to Compute the Optimal Control of a Single Stage Hybrid Manufacturing Systems. International Journal of Computational Intelligence, Volume 1, Number 3, 189-206.
  22. Kaelo P. and Ali M.M., 2007. Integrated Crossover Rules in Real Coded Genetic Algorithms. European Journal of Operational Research, Volume 176, Issue 1, 60-76.


Genetic Algorithm, Multi-Gene, Multi-parameter, Attributes selection, Attributes prediction.