Abstract
In the fields of engineering and data sciences, the optimization problems arise on regular basis. With the progress in the field of scientific computing and research, the optimization is not a problem for small data sets and lower dimensional problems. The problem arise, when the data is large, stochastic in nature, and/or multidimensional. The basic optimization tools fail for such problems due to the complexity. The genetic algorithms, based on the natural selection hypothesis, play an imperative role to deal with such complex problems. Genetic algorithms are used in the literature to optimize numerous problems. In the field of computational biology, these algorithms have provided cost effective solutions to find optimal values for large data sets. The genetic algorithms have been used for image reconstruction. These algorithms are based on sub-algorithms to improve the accuracy and precision. We will discuss the advanced genetic algorithms and their applications in detail. Genetic algorithm, in hybrid form have attracted interest of researchers from almost all fields, including computer science, applied mathematics, engineering and computational biology. These tools help to analyze the systems in a swift manner. This important feature is discussed with the aid of examples. The time series forecasting and the Bayesian inference, in combination with the genetic algorithms, can prove to be powerful artificial intelligence tools. We will discuss this important aspect in detail with the aid of some examples.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data and Code Availability
All the data and material are provided within the manuscript. Code repositories are mentioned with in the manuscript.
References
AbuJarad MH, Khan AA, Khaleel MA, AbuJarad ES, AbuJarad AH, Oguntunde PE (2020) Bayesian reliability analysis of Marshall and Olkin model. Ann Data Sci 7(3):461–489
Gramaje A, Thabtah F, Abdelhamid N, Ray SK (2019) Patient discharge classification using machine learning techniques. Ann Data Sci 1–13
Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining, vol 10. McGraw-Hill/Irwin, New York
Shi Y, Tian Y, Kou G, Peng Y, Li J (2011) Optimization based data mining: theory and applications. Springer, Berlin
Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178
Sohail A (2019) Inference of biomedical data sets using Bayesian machine learning. Biomed Eng Appl Basis Commun 31:1950030
Iftikhar M, Sohail A, Ahmad N (2019) Deterministic and stochastic analysis of dengue spread model. Biomed Eng (Singapore) 31:1950008
Sohail A, Idrees M, Sajjad M, Iftikhar S, Tunc S (2020) Computational framework to explore impact of environmental stress on epidemics. Biomed Eng (Singapore) 32:2050047
Yu Z, Sohail A, Nutini A, Arif R (2020) Delayed modeling approach to forecast the periodic behaviour of sars-2. Front Mol Biosci 7:386
Yu Z, Ellahi R, Nutini A, Sohail A, Sait SM (2021) Modeling and simulations of CoViD-19 molecular mechanism induced by cytokines storm during SARS-CoV2 infection. J Mol Liq 327:114863
Yu Z, Arif R, Fahmy MA, Sohail A (2021) Self organizing maps for the parametric analysis of COVID-19 SEIRS delayed model. Chaos Solitons Fractals 150:111202
El-Mihoub TA, Hopgood AA, Nolle L, Battersby A (2006) Hybrid genetic algorithms: a review. Eng Lett 13(2):124
Drezner Z, Drezner TD (2020) Biologically inspired parent selection in genetic algorithms. Ann Oper Res 287(1):161
Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11(2):2664
Sundararajan PK, Mengshoel OJ (2016) A genetic algorithm for learning parameters in Bayesian networks using expectation maximization. In: Conference on probabilistic graphical models, vol 1, p 511. PMLR
Das AK, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl Soft Comput 65:400
Zhang BT (2000) Bayesian methods for efficient genetic programming. Genet Program Evolvable Mach 1(3):217
Chen N, Xiong C, Du W, Wang C, Lin X, Chen Z (2019) An improved genetic algorithm coupling a back-propagation neural network model (IGA-BPNN) for water-level predictions. Water 11(9):1795
Sun Y, Xue B, Zhang M, Yen GG, Lv J (2020) Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans Cybern 50(9):3854
Lin H, Yang C, Xu X (2020) A new optimization model of CCHP system based on genetic algorithm. Sustain Cities Soc 52(101811):101811
Funding
None.
Author information
Authors and Affiliations
Contributions
AS did visulization, conception and modeling during this research.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Sohail, A. Genetic Algorithms in the Fields of Artificial Intelligence and Data Sciences. Ann. Data. Sci. 10, 1007–1018 (2023). https://doi.org/10.1007/s40745-021-00354-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40745-021-00354-9