Landscape architecture environmental adaptability evaluation model based on improved genetic algorithm

Chunyan Li

International Journal of Environmental Technology and Management, 2022, vol. 25, issue 1/2, 77-94

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.

Keywords: improved genetic algorithm; population entropy; diversity measurement; landscape environment; adaptability evaluation. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=120729 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijetma:v:25:y:2022:i:1/2:p:77-94

Access Statistics for this article

More articles in International Journal of Environmental Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().