Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021;80(5):8091-8126.
doi: 10.1007/s11042-020-10139-6. Epub 2020 Oct 31.

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

Sourabh Katoch  1 , Sumit Singh Chauhan  1 , Vijay Kumar  1
Affiliations

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

Sourabh Katoch et al. Multimed Tools Appl. 2021.

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.

Keywords: Crossover; Evolution; Genetic algorithm; Metaheuristic; Mutation; Optimization; Selection.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Classification of metaheuristic Algorithms
Fig. 2
Fig. 2
Operators used in GA
Fig. 3
Fig. 3
Swapping genetic information after a crossover point
Fig. 4
Fig. 4
Swapping genetic information between crossover points
Fig. 5
Fig. 5
Swapping individual genes
Fig. 6
Fig. 6
Partially matched crossover (PMX) [117]
Fig. 7
Fig. 7
Cycle Crossover (CX) [140]
Fig. 8
Fig. 8
Local and global optima [149]
See this image and copyright information in PMC

References

    1. Abbasi M, Rafiee M, Khosravi MR, Jolfaei A, Menon VG, Koushyar JM (2020) An efficient parallel genetic algorithm solution for vehicle routing problem in cloud implementation of the intelligent transportation systems. Journal of cloud Computing 9(6)
    1. Abdelghany A, Abdelghany K, Azadian F. Airline flight schedule planning under competition. Comput Oper Res. 2017;87:20–39.
    1. Abdulal W, Ramachandram S. Reliability-aware genetic scheduling algorithm in grid environment. Katra, Jammu: International Conference on Communication Systems and Network Technologies; 2011. pp. 673–677.
    1. Abdullah J. Multiobjectives ga-based QoS routing protocol for mobile ad hoc network. Int J Grid Distrib Comput. 2010;3(4):57–68.
    1. Abo-Elnaga Y, Nasr S. Modified evolutionary algorithm and chaotic search for Bilevel programming problems. Symmetry. 2020;12:767.

LinkOut - more resources