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Your search keyword '"Chavoya, Arturo"' showing total 40 results
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40 results on '"Chavoya, Arturo"'

5. A Parallel Algorithm for Multiple Biological Sequence Alignment

Author

Andalon-Garcia, Irma R., Chavoya, Arturo, Meda-Campaña, M. E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lones, Michael A., editor, Smith, Stephen L., editor, Teichmann, Sarah, editor, Naef, Felix, editor, Walker, James A., editor, and Trefzer, Martin A., editor

Published
2012
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6. Finding the Minimal Gene Regulatory Function in the Presence of Undefined Transitional States Using a Genetic Algorithm

Author

Chavez-Alvarez, Rocio, Chavoya, Arturo, Lopez-Martin, Cuauhtemoc, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lones, Michael A., editor, Smith, Stephen L., editor, Teichmann, Sarah, editor, Naef, Felix, editor, Walker, James A., editor, and Trefzer, Martin A., editor

Published
2012
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7. Software Development Effort Estimation in Academic Environments Applying a General Regression Neural Network Involving Size and People Factors

Author

López-Martín, Cuauhtémoc, Chavoya, Arturo, Meda-Campaña, M. E., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Martínez-Trinidad, José Francisco, editor, Carrasco-Ochoa, Jesús Ariel, editor, Ben-Youssef Brants, Cherif, editor, and Hancock, Edwin Robert, editor

Published
2011
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9. 3D Cell Pattern Generation in Artificial Development

Author

Chavoya, Arturo, Andalon-Garcia, Irma R., Lopez-Martin, Cuauhtemoc, Meda-Campaña, M. E., Kacprzyk, Janusz, editor, González, Juan R., editor, Pelta, David Alejandro, editor, Cruz, Carlos, editor, Terrazas, Germán, editor, and Krasnogor, Natalio, editor

Published
2010
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10. Artificial Development

Author

Chavoya, Arturo, Kacprzyk, Janusz, editor, Hassanien, Aboul-Ella, editor, Abraham, Ajith, editor, Vasilakos, Athanasios V., editor, and Pedrycz, Witold, editor

Published
2009
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26. Cell Pattern Generation in Artificial Development

Author

Chavoya, Arturo

Abstract

As is often the case, by studying how nature works, insight can be gained that aid in proposing approaches for solving a particular problem. In this case, it was decided that the number of enhancer and inhibitor sites in the regulatory network could be increased with respect to the original ARN model, as biological gene regulatory networks usually contain a number of such sites. Likewise, the role as enhancer or inhibitor of the regulatory sites was

Published
2008

40. An Artificial Development Model for Cell Pattern Generation.

Author

Carbonell, Jaime G., Siekmann, Jörg, Randall, Marcus, Abbass, Hussein A., Wiles, Janet, Chavoya, Arturo, and Duthen, Yves

Abstract

Cell pattern formation has a crucial role in both artificial and natural development. This paper presents an artificial development model for cell pattern generation based on the cellular automata (CA) paradigm. Cellular growth is controlled by a genome consisting of an artificial regulatory network (ARN) and a series of structural genes. The genome was evolved by a Genetic Algorithm (GA) in order to produce 2D cell patterns through the selective activation and inhibition of genes. Morphogenetic gradients were used to provide cells with positional information that constrained cellular replication. After a genome was evolved, a single cell in the middle of the CA lattice was allowed to reproduce until a cell pattern was formed. The model was applied to the problem of growing a French flag pattern. [ABSTRACT FROM AUTHOR]

Published
2007
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