Genetic Algorithm: Reviews, Implementations, and Applications

Authors

  • Tanweer Alam Islamic University of Madinah https://orcid.org/0000-0003-2731-4627
  • Shamimul Qamar King Khalid University, Abha, Saudi Arabia
  • Amit Dixit Department of ECE, Quantum School of Technology, Roorkee
  • Mohamed Benaida Islamic University of Madinah

DOI:

https://doi.org/10.3991/ijep.v10i6.14567

Keywords:

Genetic Algorithm, Search Techniques, Random Tests, Evolution, Applications.

Abstract


Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Author Biographies

Tanweer Alam, Islamic University of Madinah

Department of Computer Science

Rank: Associate Professor

Shamimul Qamar, King Khalid University, Abha, Saudi Arabia

Department of Computer Engineering

Rank: Professor

Amit Dixit, Department of ECE, Quantum School of Technology, Roorkee

Department of ECE

Rank: Dean (Research) & Professor

Mohamed Benaida, Islamic University of Madinah

Department of Information Systems

Rank: Associate Professor

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Published

2020-12-08

How to Cite

Alam, T., Qamar, S., Dixit, A., & Benaida, M. (2020). Genetic Algorithm: Reviews, Implementations, and Applications. International Journal of Engineering Pedagogy (iJEP), 10(6), pp. 57–77. https://doi.org/10.3991/ijep.v10i6.14567

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Papers