Algorithmic Pruning

Algorithmic Pruning is a backtracking optimization strategy that systematically eliminates infeasible or suboptimal branches from the search space by applying constraint-based or heuristic criteria to avoid redundant exploration.

116

Publications

5.9K

Citations

286

Authors

143

Institutions

Publications per year

2017–2026

29

Authors

286

  1. QD
    Qun Dai

    Nanjing University of Aeronautics and Astronautics, China

    5 publications · 103 citations

    5

    h-index

  2. NS
    Nathan Sturtevant

    University of Alberta, Canada

    3 publications · 97 citations

    3

    h-index

  3. NB
    Neil Burch

    University of Alberta, Canada

    2 publications · 24 citations

    2

    h-index

  4. MT
    Matthijs T. J. Spaan

    Delft University of Technology, Netherlands

    2 publications · 61 citations

    2

    h-index

  5. MW
    Martin Wehrle

    University of Basel, Switzerland

    2 publications · 36 citations

    2

    h-index

1–5 of 286

Venues

  1. Neurocomputing

    5 publications · 196 citations

    5

    h-index

  2. Applied Intelligence

    4 publications · 71 citations

    4

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 149 citations

    3

    h-index

  4. Artificial Intelligence

    3 publications · 198 citations

    3

    h-index

  5. Applied Soft Computing

    3 publications · 89 citations

    3

    h-index

1–5