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Evolutionary Algorithms and Applications

Evolutionary Algorithms and Applications is a research topic within Artificial Intelligence. Science Explorer counts 31k research works in it since 1951. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of genetic programming in machine learning, particularly in the areas of classification, feature selection, symbolic regression, and evolvable hardware. It explores the use of evolutionary algorithms and learning classifier systems to solve complex problems, with an emphasis on multiobjective optimization and semantic genetic programming.

  • Genetic Programming
  • Machine Learning
  • Classification
  • Evolutionary Algorithms
  • Feature Selection
  • Symbolic Regression
  • Evolvable Hardware
  • Learning Classifier Systems
  • Semantic Genetic Programming
  • Multiobjective Optimization
Research works
31k
fractional, since 1951
In the world top 10%
3k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+5%
the tick is no change
Largest producer
China
36% of the 10 largest, 2022-now
Evolutionary Algorithms and Applications

Which countries lead Evolutionary Algorithms and Applications research?

By volume, China and the United States publish the most (1.2k and 592 works in 2022-now).

By volume, 2022-now

  1. 1 China 1.2k works
  2. 2 United States 592 works
  3. 3 India 524 works
  4. 4 United Kingdom 199 works
  5. 5 Germany 179 works
  6. 6 Japan 167 works
  7. 7 France 120 works
  8. 8 Spain 112 works
  9. 9 Mexico 108 works
  10. 10 Brazil 98 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 36.4%United States: 18.0%India: 15.9%United Kingdom: 6.0%6 others listed: 23.7%36%largest
China1,201 · 36.4%United States592 · 18.0%India524 · 15.9%United Kingdom199 · 6.0%6 others listed783 · 23.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Evolutionary Algorithms and Applications research?

By volume in 2022-now, Victoria University of Wellington publishes the most Evolutionary Algorithms and Applications research, followed by Centre National de la Recherche Scientifique and Southern University of Science and Technology.

By volume, 2022-now

  1. 1 Victoria University of WellingtonNew Zealand 47 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 37 works
  3. 3 Southern University of Science and TechnologyChina 35 works
  4. 4 Xidian UniversityChina 27 works
  5. 5 Chinese Academy of SciencesChina 26 works
  6. 6 Michigan State UniversityUnited States 25 works
  7. 7 Northeastern UniversityChina 21 works
  8. 8 South China University of TechnologyChina 20 works
  9. 9 Shenzhen UniversityChina 19 works
  10. 10 Universidad de GuadalajaraMexico 19 works

Who are the leading researchers in Evolutionary Algorithms and Applications?

The most-cited researchers publishing on Evolutionary Algorithms and Applications include Eric S. Lander, Yoshua Bengio and Geoffrey E. Hinton.

  1. 1 Eric S. Lander United States 524k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Geoffrey E. Hinton Canada 454k citations
  4. 4 George Davey Smith United Kingdom 435k citations
  5. 5 Steven L. Salzberg United States 372k citations
  6. 6 Karl John Friston United Kingdom 310k citations
  7. 7 Sudhir Kumar United States 295k citations
  8. 8 Koichiro Tamura Japan 268k citations
  9. 9 Mark I. McCarthy United Kingdom 267k citations
  10. 10 Yann LeCun United States 256k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Evolutionary Algorithms and Applications?

The most cited paper in Evolutionary Algorithms and Applications is "A fast and elitist multiobjective genetic algorithm: NSGA-II" (Kalyanmoy Deb, Amrit Pratap, Sakshi Agarwal et al., 2002), with 49,664 citations. Next come "MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms" (39,563) and "Statistical Learning Theory" (26,900).

Most cited papers
#PaperReleasedCitations
1 A fast and elitist multiobjective genetic algorithm: NSGA-II Kalyanmoy Deb, Amrit Pratap, Sakshi Agarwal et al. · IEEE Transactions on Evolutionary Computation Apr 2002 49,664
2 MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms Sudhir Kumar, Glen Stecher, Michael M. Li et al. · Molecular Biology and Evolution Open access May 2018 39,563
3 Statistical Learning Theory Yuhai Wu, Vladimir N. Vapnik · Technometrics Nov 1999 26,900
4 Grey Wolf Optimizer Seyedali Mirjalili, Seyed Mohammad Mirjalili, Seyed Mohammad Mirjalili et al. · Advances in Engineering Software Open access 21 Jan 2014 19,320
5 Array programming with NumPy Cr Harris et al. · RePEc: Research Papers in Economics Open access 17 Sep 2020 18,804
All 100 most cited papers, with filters and sorting

Where is Evolutionary Algorithms and Applications research done?

The largest centres of Evolutionary Algorithms and Applications research in 2022-now are Beijing (China), Xi'an (China), Shenzhen (China) and Guangzhou (China). Among places with at least 20 works in it, it is an unusually large share of all research in Wellington.

Largest cities, 2022-now

  1. 1 BeijingChina 138 works
  2. 2 Xi'anChina 77 works
  3. 3 ShenzhenChina 71 works
  4. 4 GuangzhouChina 65 works
  5. 5 ShanghaiChina 60 works
  6. 6 WuhanChina 54 works
  7. 7 NanjingChina 51 works
  8. 8 WellingtonNew Zealand 48 works
  9. 9 TokyoJapan 47 works
  10. 10 LondonUnited Kingdom 42 works

Where it is the local speciality

  1. WellingtonNZ · 47.7 works26×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Evolutionary Algorithms and Applications than the world average.

See Evolutionary Algorithms and Applications on the map

Where is the best place to study Evolutionary Algorithms and Applications?

Among universities, judged by research, Fujian University of Technology, Xidian University and Nanjing University of Information Science and Technology score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 25.97%fractional works in this field (log) →share of work in the world top 10% →Fujian University of…Nanjing University o…Xidian UniversityVictoria University …
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Fujian University of TechnologyChina 66.450.0%26.5×11 +74.3%
2Xidian UniversityChina 58.120.5%8.9×27 +64.0%
3Nanjing University of Information Science and TechnologyChina 57.930.3%8.5×14 +218.8%
4Victoria University of WellingtonNew Zealand 57.313.2%52.1×47 -22.3%
5Hong Kong Polytechnic UniversityChina 57.140.2%3.0×11 +124.8%
6Southern University of Science and TechnologyChina 54.612.5%15.4×34 -11.0%
7Northeastern UniversityChina 54.022.1%5.5×21 +82.0%
8Anhui UniversityChina 52.525.4%7.2×14 +181.4%
9China University of GeosciencesChina 51.325.6%8.1×16 +30.1%
10Shenzhen UniversityChina 51.019.9%6.2×19 +7.1%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Evolutionary Algorithms and Applications, country by country

Is Evolutionary Algorithms and Applications research growing?

Output in 2022-2025 was 5% higher than in 2018-2021, peaking in 2024. The fastest-growing topics are Evolutionary Algorithms and Applications.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Evolutionary Algorithms and Applications as a percentage of every work published that year, 0.023% in 2024.

still counting0.025%0.050%0.075%Evolutionary Algorithms and Applications0.020% in 202519801990200020102020

Fractional works of Evolutionary Algorithms and Applications over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Evolutionary Algorithms and Applications10.3% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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