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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.


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