Genetic Algorithm Site Layout Optimization

Genetic Algorithm Site Layout Optimization is a computational method that belongs to the class of evolutionary optimization techniques, distinguished by its use of genetic algorithm principles—such as selection, crossover, and mutation—to systematically generate and refine site layout configurations that optimize spatial efficiency, resource accessibility, and operational safety in construction and industrial engineering projects.

71

Publications

5.3K

Citations

174

Authors

97

Institutions

Publications per year

2017–2026

15

Authors

174

  1. CM
    C. M. Tam

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 415 citations

    4

    h-index

  2. TH
    Tarek Hegazy

    University of Waterloo, Canada

    3 publications · 299 citations

    3

    h-index

  3. AK
    Ahmed Khalafallah

    University of Central Florida, United States

    3 publications · 224 citations

    3

    h-index

  4. EE
    Emad Elbeltagi

    Mansoura University, Egypt

    3 publications · 299 citations

    3

    h-index

  5. KE
    Khaled El‐Rayes

    University of Illinois Urbana-Champaign, United States

    3 publications · 224 citations

    3

    h-index

1–5 of 174

Venues

  1. 9

    h-index

  2. Automation in Construction

    8 publications · 682 citations

    8

    h-index

  3. Renewable Energy

    4 publications · 1.3K citations

    4

    h-index

  4. Journal of Computing in Civil Engineering

    3 publications · 364 citations

    3

    h-index

  5. 3

    h-index

1–5