Evidence map›Paper›PMID 33162782›Full record

ArticleMultimedia tools and applications2021

A review on genetic algorithm: past, present, and future.

Sourabh Katoch, Sumit Singh Chauhan, Vijay Kumar

Abstract read
In one paragraph

Article in Multimedia tools and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 323 papers.

0numbers the graph read from it
0cells of the map it votes in
323citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

323 citing papers in PubMed.

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263 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sourabh KatochComputer Science and Engineering Department, National Institute of Technology, Hamirpur, India.
Sumit Singh ChauhanComputer Science and Engineering Department, National Institute of Technology, Hamirpur, India.
Vijay KumarComputer Science and Engineering Department, National Institute of Technology, Hamirpur, India.ORCID 0000-0002-3460-6989

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

Indexed as

CrossoverEvolutionGenetic algorithmMetaheuristicMutationOptimizationSelection

Identifiers

PMID33162782
PMCPMC7599983

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.