Directed Evolution Optimization

Directed Evolution Optimization is a computational optimization method that belongs to the class of evolutionary algorithms, distinguished by its systematic incorporation of experimental feedback from successive generations of variant selection to guide the search process toward improved fitness peaks in a high-dimensional landscape.

228

Publications

14.4K

Citations

1.2K

Authors

352

Institutions

Publications per year

2017–2026

71

Authors

1.2K

  1. FH
    Frances H. Arnold

    California Institute of Technology, United States

    7 publications · 977 citations

    7

    h-index

  2. DS
    Dan S. Tawfik

    Weizmann Institute of Science, Israel

    6 publications · 891 citations

    6

    h-index

  3. HZ
    Huimin Zhao

    University of Illinois Urbana-Champaign, United States

    5 publications · 208 citations

    5

    h-index

  4. US
    Ulrich Schwaneberg

    RWTH Aachen University, Germany

    5 publications · 241 citations

    5

    h-index

  5. RE
    Robert E. Campbell

    University of Alberta, Canada

    5 publications · 562 citations

    5

    h-index

1–5 of 1.2K

Venues

  1. Applied Microbiology and Biotechnology

    15 publications · 644 citations

    14

    h-index

  2. Journal of Biotechnology

    13 publications · 873 citations

    13

    h-index

  3. Journal of Molecular Biology

    JMB · 9 publications · 843 citations

    9

    h-index

  4. Journal of Biological Chemistry

    JBC · 9 publications · 378 citations

    9

    h-index

  5. Chemistry & Biology

    8 publications · 554 citations

    8

    h-index

1–5