Multiobjective Genetic Algorithms

Multiobjective Genetic Algorithms is a computational optimization framework within evolutionary computation that extends genetic algorithms to simultaneously optimize multiple conflicting objectives by maintaining a population of solutions and using mechanisms such as nondominated sorting and diversity preservation to converge toward a Pareto-optimal front.

1.7K

Publications

157.1K

Citations

4.4K

Authors

1.4K

Institutions

Publications per year

2017–2026

437

Variants

MogaMulti-objective Genetic Algorithms

Authors

4.4K

  1. NC
    Nirupam Chakraborti

    Indian Institute of Technology Kharagpur, India

    27 publications · 1K citations

    18

    h-index

  2. HI
    Hisao Ishibuchi

    Osaka Prefecture University, Japan

    19 publications · 3.4K citations

    15

    h-index

  3. MG
    Mitsuo Gen

    Ashikaga University, Japan

    16 publications · 1.4K citations

    15

    h-index

  4. PF
    P.J. Fleming

    University of Sheffield, United Kingdom

    17 publications · 2.9K citations

    14

    h-index

  5. SK
    Santosh K. Gupta

    Indian Institute of Technology Kanpur, India

    14 publications · 698 citations

    13

    h-index

1–5 of 4.4K

Venues

  1. Expert Systems with Applications

    33 publications · 2.9K citations

    26

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  2. 24

    h-index

  3. Applied Soft Computing

    25 publications · 1.3K citations

    20

    h-index

  4. Information Sciences

    15 publications · 1.5K citations

    15

    h-index

  5. Reliability Engineering & System Safety

    16 publications · 4.5K citations

    15

    h-index

1–5