Genetic Algorithm and Applications: A Study of Intelligent Systems

This report provides a comprehensive analysis of the research paper "A Study on Genetic Algorithm and its Applications." The paper explores the concept and applications of the genetic algorithm, an optimization method based on natural evolution. The report examines the paper's content, including its focus on the genetic algorithm's operations (selection, crossover), its use in solving real-world problems, and its potential for reducing system complexity. The research methods, including qualitative research design, inductive approach, and secondary data collection (literature review), are critically evaluated. The report also highlights the paper's findings on the genetic algorithm's ability to simulate and evaluate living organisms, and its operations. The study also discusses the problems highlighted by the authors, such as the impact of space on the genetic algorithm process. Finally, the report summarizes the paper's conclusion that the genetic algorithm is effective for solving complex problems and its implications for intelligent systems.

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A Study on Genetic Algorithm
and its Applications

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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

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performing effectively. The research provided their views and identified that there are various
operations include in the genetic algorithm for example selection operation, crossover operation,
and uniform operation and so on. The conducted literature review identified gaps in the research
and reviewed various applications of genetic algorithms that helped the authors for performing
investigation effectively (Metawa, Hassan, & Elhoseny, 2017). The term fitness function defines
the criterion for ranking potential hypotheses developed in the research and manages each step
effectively.
It is observed that the genetic algorithm can be used for dissimilarity measures and able
to enhance the effectiveness of the K-modes algorithm that is an extension of k-means (Cerrada,
et al., 2016). The authors observed that the genetic algorithm is an effective process that can be
used for solving real-world issues and reduce complexity from the systems appropriately. In the
context of the genetic algorithm, the search space includes various strings and the objective
purpose is called as the fitness value. In this paper, the authors also explained the genetic
algorithm for finding a space of candidate solution that may help the readers for understanding
the process of genetic algorithm (Yuan, Elhoseny, El-Minir, & Riad, 2017).
Research methods
It is the specific process which are mainly used for finding and analyzing information
about the research topic. Mainly, research methodology enables the researchers to critically
evaluating research in terms of reliability and validity. From this paper it is found that the
authors adopted several research methods which are described below:
 Research design
 Research approach
 Data collection
 Data analysis
Research design
It has the ability to control and manage the errors that occurred in the research and help
the researchers for emerging a real hypothesis. In this article, the authors included the qualitative
type of design because the genetic algorithm is a theoretical topic and the authors provided
qualitative data about the research topic. Using such design the authors were capable to reduce

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gaps and produce effective questions and arguments related to the genetic algorithm (Gai, Qiu, &
Zhao, 2016).
Research approach
It is defined as a plan or strategy which includes steps and methods for conducting
research effectively. There are two types of approaches mainly involved in the research, for
example, inductive and deductive that depends on the nature of the investigation (Hiassat,
Diabat, & Rahwan, 2017). In this article, the authors adopted the only inductive type of approach
as it is able to help the authors for conducting research in the right direction and produce
appropriate arguments and hypotheses related to genetic algorithms.
Data collection
From this paper, it is identified that the authors produced a literature appraisal and
included viewpoints of other authors that show that the researchers adopted a secondary data
gathering process rather than primary. The major advantage of this technique is that it provides
all relevant facts and reduces the gaps related to the research questions. There are various
processes used by the authors for collecting secondary data including literature review, journal
papers, websites, books, and others. The adopted data collection methods are able to reduce
issues and problems faced by the researchers and helped in the enhancement of research quality.
Moreover, the researchers included the findings of previous papers and resolved the limitations
of recent articles.
Data analysis
It is a kind of technique which is able to evaluate the data using statistical and analytical
tools in order to receive useful information in the investigation. There are two kinds of analysis
methods used including content examination and arithmetical examination. In this journal paper,
the authors used a content analysis method due to the theoretical behavior of the research topic.
The selected method is able to provide better outcomes of the investigation and resolved the
queries of the participants. Therefore, it is argued that content analysis and SPSS tools both
helped the authors for achieving the aims of the investigation and obtaining an effective
conclusion at the end of the study.

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Findings of the paper
It is found that genetic is the best algorithm that has the potential to simulate and evaluate
the living organisms. This research paper shows the importance of the genetic algorithm and
reviewed the various operations used in the genetic algorithm. Moreover, such kind of algorithm
uses the concept of the natural evolution process and helps the consumers for reducing
complexities from the developed networks (Deng, Liu, & Zhou, 2015). For better understanding
and analyzing the concept of genetic algorithm, the authors also included a program for finding
the space of candidate solutions. The authors observed that selection operation is an effective
process that can be used for solving the issues.
Problems highlighted by the authors
The authors highlighted that the genetic algorithm has the capacity to decrease the
problems related to the individual population. In which space is a key issue associated with the
genetic algorithm that can impact on the entire process. The researchers also observed that the
recent selection technique will have issues when the fitness’s differ larger but this study provided
effective solutions and techniques (Li, & Gao, 2016).
Results of the paper
This paper shows the concept behind the genetic algorithm and identified the numerous
applications of the GA process. It is obtained that genetic algorithms ate search and optimization
programs that use natural evolution principles. This research provided a platform for the students
for understanding how the genetic algorithm is combined with numerous techniques and
processes in order to drive optimal solutions.
Conclusion of the article
The authors concluded that a genetic algorithm proved to be an effective process for
finding areas of complex and solving real-world problems in an effective manner. This research
paper reviewed and evaluated the genetic algorithm and also examined the various operations
used in this computing program. The authors provided their opinions and evaluated that such
kinds of programs are adaptive for solving real-world problems and helps for changing the

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environment effectively. This paper is related to the unit because the authors highlighted
viewpoints on intelligent systems and genetic algorithms.
Conclusion
This paper studied a research article that is grounded on the intelligent scheme and it can
be concluded that genetic algorithm may be used for solving more complex problems and real-
time issues effectively. The findings and research methods used by the authors were also
reviewed that may help the scholars to sympathetic the intent of the authors and the significance
of the genetic algorithm in the context of the environment.

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References
Cerrada, M., Zurita, G., Cabrera, D., Sánchez, R. V., Artés, M., & Li, C. (2016). Fault diagnosis
in spur gears based on genetic algorithm and random forest. Mechanical Systems and
Signal Processing, 70, 87-103.
Deng, Y., Liu, Y., & Zhou, D. (2015). An improved genetic algorithm with an initial population
strategy for symmetric TSP. Mathematical Problems in Engineering, 2015.
Gai, K., Qiu, M., & Zhao, H. (2016). Cost-aware multimedia data allocation for heterogeneous
memory using a genetic algorithm in cloud computing. IEEE transactions on cloud
computing.
Haldurai, L., Madhubala, T., & Rajalakshmi, R. (2016). A Study on Genetic Algorithm and its
Applications, JCSE, 4(10), 12-18.
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for a location-
inventory-routing problem with perishable products. Journal of manufacturing
systems, 42, 93-103.
Kramer, O. (2017). Genetic algorithm essentials (Vol. 679). Springer.
Li, X., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job-
shop scheduling problems. International Journal of Production Economics, 174, 93-110.
Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm-based model for
optimizing bank lending decisions. Expert Systems with Applications, 80, 75-82.
Qiu, M., Ming, Z., Li, J., Gai, K., & Zong, Z. (2015). Phase-change memory optimization for the
green cloud with a genetic algorithm. IEEE Transactions on Computers, 64(12), 3528-
3540.
Yuan, X., Elhoseny, M., El-Minir, H. K., & Riad, A. M. (2017). A genetic algorithm-based,
dynamic clustering method towards improved WSN longevity. Journal of Network and
Systems Management, 25(1), 21-46.