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A SAND approach based on cellular computation models for analysis and optimization

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dc.contributor.author Canyurt, O.
dc.contributor.author Hajela, P.
dc.date.accessioned 2019-08-16T11:40:01Z
dc.date.available 2019-08-16T11:40:01Z
dc.date.issued 2004
dc.identifier.issn 02734508 (ISSN)
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/5012
dc.description.abstract Genetic algorithms have received considerable recent attention in problems of design optimization. The mechanics of population-based search in GA's is highly amenable to implementation on parallel computers. The present paper describes a fine-grained model of parallel GA implementation that derives from a cellular-automata-like computation. The central idea behind the cellular genetic algorithm (CGA) approach is to treat the GA population as being distributed over a 2-D grid of cells, with each member of the population occupying a particular cell and defining the state of that cell. Evolution of the cell state is tantamount to updating the design information contained in a cell site, and as in cellular automata computations, takes place on the basis of local interaction with neighboring cells. A special focus of the paper is in the use of cellular automata (CA) based models for structural analysis in conjunction with the CGA approach to optimization. In such an approach, the analysis and optimization are evolved simultaneously in a unified cellular computational framework. The paper describes the implementation of this approach and examines its efficiency in the context of representative structural optimization problems. Copyright © 2004 by Prabhat Hajela.
dc.language.iso English
dc.subject Cellular computation models
dc.subject Cellular genetic algorithms (CGA)
dc.subject Nonlinear stiffness
dc.subject Turning machines
dc.subject Automata theory
dc.subject Computer simulation
dc.subject Mathematical models
dc.subject Parallel processing systems
dc.subject Problem solving
dc.subject Strain
dc.subject Structural optimization
dc.subject Vectors
dc.subject Genetic algorithms
dc.title A SAND approach based on cellular computation models for analysis and optimization
dc.type Conference Paper
dc.identifier.volume 6
dc.identifier.startpage 4228
dc.identifier.endpage 4241
dc.identifier.index Scopus


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