Engineering designers are increasingly looking to automate the design process. By utilizing tools to support decision making engineers can explore a wider range of options and reduce the time to an initial design, which can then be iterated over a number of cycles. One area which is prominent in the literature is the design of boat layouts as this is a complex part of the design which interacts with a range of different subsystems. The layout is often optimized using Genetic Algorithms and the selection of these algorithms has been shown to be important. In addition, the representation for this problem is also vital, with a balance between the continuous nature of the design space and the total size of the space. This paper therefore explores one representation, the vector-based approach, to determine how reducing the number of constraints might improve the performance. The new representation is compared over 3 problems of increasing complexity using a number of current state-of-the-art Genetic Algorithms. The results show that the reduction in constraints increases the size of the space more than it reduces the complexity, showing poorer results than previously. cMLSGA is always the top performing Genetic Algorithm and it can be concluded that the diversity focused general solvers should be promoted on similar kind of problems.


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    Titel :

    Genetic Algorithm Selection for Ship Concept Design


    Weitere Titelangaben:

    Lecture Notes in Civil Engineering


    Beteiligte:
    Okada, Tetsuo (Herausgeber:in) / Suzuki, Katsuyuki (Herausgeber:in) / Kawamura, Yasumi (Herausgeber:in) / Sobey, Adam (Autor:in) / Grudniewski, Przemyslaw (Autor:in) / Savasta, Thomas (Autor:in)

    Kongress:

    Practical Design of Ships and Other Floating Structures ; 2019 ; Yokohama, Japan September 22, 2019 - September 26, 2019



    Erscheinungsdatum :

    2020-10-04


    Format / Umfang :

    17 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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