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TR-Dizin İndeksli Yayınlar Koleksiyonu

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Browsing TR-Dizin İndeksli Yayınlar Koleksiyonu by Publisher "Association for Scientific Research membranes@mdpi.com"

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    Citation - Scopus: 10
    A genetic algorithm to solve the multidimensional Knapsack problem
    (Association for Scientific Research membranes@mdpi.com, 2013-12-01) Murat Erşen Berberler; Asli Guler; Urfat Nuriyev; Berberler, Murat Ersen; Guler, Asli; Nuriyev, Urfat G.
    In this paper The Multidimensional Knapsack Problem (MKP) which occurs in many different applications is studied and a genetic algorithm to solve the MKP is proposed. Unlike the technique of the classical genetic algorithm initial population is not randomly generated in the proposed algorithm thus the solution space is scanned more efficiently. Moreover the algorithm is written in C programming language and is tested on randomly generated instances. It is seen that the algorithm yields optimal solutions for all instances. © 2020 Elsevier B.V. All rights reserved.
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    A proposed model for candidate selection process in political parties based on fuzzy logic methodology
    (Association for Scientific Research membranes@mdpi.com, 2012-08-01) Yılmaz Gökşen; Onur Doǧan; Mete Eminaǧaoǧlu
    Classical logic and classical set theorems are not sufficient enough when it is necessary to deal with complex decision making problems which also involve human experiences. Some researches suggest that senior management usually makes intuitive decisions in the process of selecting the candidates in political parties which brings out the need to derive a new efficient robust and applicable method. In this study the qualitative characteristics and their significance level which could be used for the candidate selection process in political parties are determined. The candidate selection process consisting vague inputs is analyzed by fuzzy logic methodology and a quantitative final score has been determined for the candidate. It has been shown that the model provided some realistic and promising results which could enable further studies to derive more optimized and enhanced models for similar purposes. © 2020 Elsevier B.V. All rights reserved.