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Cellular genetic algorithm technique for the multicriterion design optimization

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Abstract

There have been increased activities in the study of genetic algorithms (GA) for problems of design optimization. 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 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 focus of the paper is in the adaptation of the cellular genetic algorithm approach in the solution of multicriteria design optimization problems. The proposed paper describes the implementation of this approach and examines its efficiency in the context of representative design optimization problems.

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Authors and Affiliations

  1. Mechanical Engineering Department, Engineering Faculty, Pamukkale University, 20020 Kinikli, Denizli, Turkey

    Olcay Ersel Canyurt

  2. Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Prabhat Hajela

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  1. Olcay Ersel Canyurt
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  2. Prabhat Hajela
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Correspondence to Olcay Ersel Canyurt.

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Canyurt, O.E., Hajela, P. Cellular genetic algorithm technique for the multicriterion design optimization. Struct Multidisc Optim 40, 201–214 (2010). https://doi.org/10.1007/s00158-008-0351-3

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