• Dynamic allocation of data-obj...

Result: Dynamic allocation of data-objects in the web, using self-tuning Genetic Algorithms

Title:
Dynamic allocation of data-objects in the web, using self-tuning Genetic Algorithms
Authors:
PEREZ O, Joaquin, PAZOS R, Rodolfo A, MORA O, Graciela, CASTILLA V, Guadalupe, MARTINEZ, José A, LANDERO N, Vanesa, FRAIRE H, Héctor, GONZALEZ B, Juan J
Source:
Advances in artificial intelligence (Sao Luis, 29 September - 1 October 2004)Lecture notes in computer science. :376-384
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 8 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Allocation dynamique, Dynamic allocation, Asignación dinámica, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Autocontrôle, Self control, Autocontrol, Charge travail, Workload, Carga trabajo, Commande autoajustable, Selftuning control, Control autoajustable, Commande processus, Process control, Control proceso, Condition opératoire, Operating conditions, Condición operatoria, Extensibilité, Scalability, Estensibilidad, Formule mathématique, Mathematical formula, Fórmula matemática, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Internet, Modèle donnée, Data models, Méthode combinatoire, Combinatorial method, Método combinatorio, Méthode heuristique, Heuristic method, Método heurístico, Méthode optimisation, Optimization method, Método optimización, Optimisation combinatoire, Combinatorial optimization, Optimización combinatoria, Réseau web, World wide web, Red WWW, Résolution problème, Problem solving, Resolución problema
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET), AP 5-164, Cuernavaca, Mor. 62490, Mexico
Instituto Tecnológico de Ciudad Madero, Mexico
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16177006
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.16177006
Database:
PASCAL Archive

Further Information

In this paper, a new mechanism for automatically obtaining some control parameter values for Genetic Algorithms is presented, which is independent of problem domain and size. This approach differs from the traditional methods which require knowing the problem domain first, and then knowing how to select the parameter values for solving specific problem instances. The proposed method uses a sample of problem instances, whose solution allows to characterize the problem and to obtain the parameter values. To test the method, a combinatorial optimization model for data-object allocation in the Web (known as DFAR) was solved using Genetic Algorithms. We show how the proposed mechanism allows to develop a set of mathematical expressions that relates the problem instance size to the control parameters of the algorithm. The expressions are then used, in on-line process, to control the parameter values. We show the last experimental results with the self-tuning mechanism applied to solve a sample of random instances that simulates a typical Web workload. We consider that the proposed method principles must be extended to the self-tuning of control parameters for other heuristic algorithms.