IDEAS home Printed from https://ideas.repec.org/a/ids/ijetma/v25y2022i1-2p77-94.html
   My bibliography  Save this article

Landscape architecture environmental adaptability evaluation model based on improved genetic algorithm

Author

Listed:
  • Chunyan Li

Abstract

In view of the fact that many environmental factors of landscape architecture are not considered in the evaluation model of landscape architecture environmental adaptability, which leads to low accuracy and long evaluation time, an evaluation model of landscape architecture environmental adaptability based on improved genetic algorithm is proposed. The real vector coding is used to select individual population, and the maximum and average fitness difference is solved to obtain the population entropy; the iterative crossover probability and mutation probability are used to realise the adaptive adjustment, and the genetic algorithm is improved by combining with the optimal retention strategy. Based on the improved adaptive genetic algorithm, the environmental adaptability evaluation system and membership functions were constructed, the weight of landscape environmental factors was determined, and the landscape environmental adaptability evaluation model was designed. The results show the highest accuracy is 95%, and the shortest evaluation time is 0.39 s.

Suggested Citation

  • Chunyan Li, 2022. "Landscape architecture environmental adaptability evaluation model based on improved genetic algorithm," International Journal of Environmental Technology and Management, Inderscience Enterprises Ltd, vol. 25(1/2), pages 77-94.
    • Handle: RePEc:ids:ijetma:v:25:y:2022:i:1/2:p:77-94
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=120729
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijetma:v:25:y:2022:i:1/2:p:77-94. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=11 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.