Created by W.Langdon from gp-bibliography.bib Revision:1.6567

- @Article{Nazari:2013:ESA,
- author = "Ali Nazari and F. {Pacheco Torgal}",
- title = "Modeling the compressive strength of geopolymeric binders by gene expression programming-GEP",
- journal = "Expert Systems with Applications",
- year = "2013",
- volume = "40",
- number = "14",
- pages = "5427--5438",
- keywords = "genetic algorithms, genetic programming, Gene expression programming, Geopolymers, Compressive strength",
- ISSN = "0957-4174",
- URL = "http://www.sciencedirect.com/science/article/pii/S0957417413002510",
- DOI = "doi:10.1016/j.eswa.2013.04.014",
- size = "12 pages",
- abstract = "Abstract GEP has been employed in this work to model the compressive strength of different types of geopolymers through six different schemes. The differences between the models were in their linking functions, number of genes, chromosomes and head sizes. The curing time, Ca(OH)2 content, the amount of superplasticizer, NaOH concentration, mold type, aluminosilicate source and H2O/Na2O molar ratio were the seven input parameters considered in the construction of the models to evaluate the compressive strength of geopolymers. A total number of 399 input-target pairs were collected from the literature, randomly divided into 299 and 100 sets and were trained and tested, respectively. The best performance model had 6 genes, 14 head size, 40 chromosomes and multiplication as linking function. This was shown by the absolute fraction of variance, the absolute percentage error and the root mean square error. These were of 0.9556, 2.4601 and 3.4716 for training phase, respectively and 0.9483, 2.8456 and 3.7959 for testing phase, respectively. However, another model with 7 genes, 12 head size, 30 chromosomes and addition as linking function showed suitable results with the absolute fraction of variance, the absolute percentage error and the root mean square of 0.9547, 2.5665 and 3.4360 for training phase, respectively and 0.9466, 2.8020 and 3.8047 for testing phase, respectively. These models showed that gene expression programming has a strong potential for predicting the compressive strength of different types of geopolymers in the considered range.",
- }

Genetic Programming entries for Ali Nazari Fernando Pacheco Torgal