Browsing by Author "Karunarathna, CDD"

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    item: Conference-Extended-Abstract
    Optimization of newspaper pagination using the simulated annealing algorithm and the genetic algorithm
    (2010) Sirisena, KJL; Perera, KAPS; Karunarathna, CDD; Hettimulla, HATD; Weerawarana, S; Koggalage, R
    Newspaper pagination has become an NP-hard problem with the need to optimized the space of a newspaper. A well paginated newspaper is a newspaper which includes a high number of advertisements and articles along with pagination rules. The research problem is to find an efficient algorithm to generate a well paginated newspaper. Most of the literature related to newspaper pagination indicated the use of the Simulated Annealing (SA) algorithm to solve the problem. In this research study, we introduce an improved method of using the Genetic Algorithm (GA) to solve the problem along with a method of deriving as improved solution using SA. This research study also includes a comparison of statistical data from the two algorithms.
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    item: Conference-Full-text
    Optimization of newspaper pagination using the simulated annealing algorithm and the genetic algorithm
    (Computer Science & Engineering Society c/o Department of Computer Science and Engineering, University of Moratuwa., 2010-09) Sirisena, KJL; Perera, KAPS; Karunarathna, CDD; Hettimulla, HATD; Weerawarana, S; Koggalage, R; Gunasekara, C; Wijegunawardana, P; Pavalanathan, U
    Newspaper pagination has become an NP-hard problem with the need to optimize the space of a newspaper. A well paginated newspaper is a newspaper which includes a high number of advertisements and articles along with specific pagination rules. The research problem is to find an efficient and suitable algorithm to generate a well paginated newspaper. Most of the literature related to newspaper pagination indicates the use of the Simulated Annealing algorithm to solve the problem. In this research study, we introduce an improved method of using the Genetic Algorithm to solve the newspaper pagination problem along with a method of deriving an improved solution using Simulated Annealing. We use some heuristic methods within the Genetic Algorithm and the Simulated Annealing algorithm to achieve the basic pagination rules. This research study includes a comparison of statistical data from the two algorithms.