A Study on Genetic Algorithm and its Applications

INTELLIGENT SYSTEM 0
A Study on Genetic
Algorithm and its
Applications

INTELLIGENT SYSTEM 1
Table of Contents
Introduction.................................................................................................................... 2
Content of the paper......................................................................................................... 2
Research methods............................................................................................................ 3
Research design........................................................................................................... 3
Research approach........................................................................................................ 4
Data collection............................................................................................................. 4
Data analysis............................................................................................................... 4
Findings of the paper........................................................................................................ 5
Problems highlighted by the authors...................................................................................... 5
Results of the paper.......................................................................................................... 5
Conclusion of the article.................................................................................................... 5
Conclusion..................................................................................................................... 6
References..................................................................................................................... 7

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Introduction
The genetic algorithm is a kind of computing program that is grounded on the values of
natural, evolution that were first developed in the year 1970. In this generation, the use of the
genetic algorithm is growing rapidly that has the ability to provide optimization related services
to the consumers. It is identified that such kind of algorithm also implements the optimization
strategies and plans by pretending the development of species using natural selection processes.
The aim of this article is to analyze the key aspects of the genetic algorithm and critique a
research paper based on the genetic algorithm. The chosen paper for this research is “A Study on
Genetic Algorithm and its Applications” that was written by Haldurai and other authors in 2016
(Haldurai, Madhubala, & Rajalakshmi, 2016). The key focus of this study is to identify the
intention and findings of this article. There are various points will be included in this study, for
example, the gratified of the article, research methods, findings, tinted issues, conclusion and so
on.
Content of the paper
This paper shows the concept behind the genetic algorithm and evaluates the various
applications of genetic applications. According to the authors, the GA is a kind of optimization
method that is based on the natural evolution process. It is mainly, composed of two kinds of
processes including the selection of the consumer for the production of the next-generation and
manipulation of the chosen consumer in order to develop the next generation effectively (Qiu,
Ming, Gai, & Zong, 2015). The selection process is able to provide better results of the
consumers and the key principle of the chosen strategy is the better is an individual. The authors
developed and implemented a literature review that provided all relevant facts and information
about the genetic algorithm along with the applications. The flow chart highlighted by the
authors included several factors such as populace initialization, fitness control, crossover,
change, stayer assortment and dismiss and reappearance best outcomes (Kramer, 2017).
The authors argued that the genetic algorithm is a kind of computing program which is
able to simulate the heredity and evaluate living organisms. It is argued that the genetic
algorithm is not simple in the optimization that requires proper system and communication for