Heuristic Refinement

Heuristic Refinement is a systematic methodological contribution that enhances the precision, applicability, or effectiveness of heuristic evaluation principles through the iterative adaptation or optimization of heuristics based on empirical feedback, theoretical analysis, or contextual specialization.

37

Publications

1.7K

Citations

86

Authors

54

Institutions

Publications per year

2017–2026

7

Authors

86

  1. MF
    Matteo Fischetti

    University of Padua, Italy

    2 publications · 140 citations

    2

    h-index

  2. EM
    Elitza Maneva

    University of California, Berkeley, United States

    2 publications · 105 citations

    2

    h-index

  3. BG
    Bruce G. Buchanan

    Stanford University, United States

    2 publications · 51 citations

    2

    h-index

  4. AA
    Albert Atserias

    Universitat Politècnica de Catalunya, Spain

    2 publications · 105 citations

    2

    h-index

  5. OJ
    Oleg Jardetzky

    Resonance Research (United States), United States

    2 publications · 51 citations

    2

    h-index

1–5 of 86

Venues

  1. SIAM Journal on Computing

    1 publications · 90 citations

    1

    h-index

  2. National Conference on Artificial Intelligence

    Conference · 1 publications · 120 citations

    1

    h-index

  3. 1

    h-index

  4. Operations Research

    1 publications · 45 citations

    1

    h-index

  5. 1

    h-index

1–5