Research Report: Applications of Genetic Engineering in Computing

This research report provides a comprehensive overview of Genetic Algorithm (GA) applications across various domains. The report begins with an introduction to GA, emphasizing its role as a common evolutionary technique for solving NP-hard problems. It then discusses the applications of GA in software engineering, exploring its use in software metrics, testing, and quality assurance. The report also covers GA's role in distributed computing, particularly in task scheduling and query optimization. Furthermore, it delves into the applications of GA in machine learning and data mining, highlighting its use in artificial neural networks, stock market analysis, and natural language processing. The report emphasizes the benefits of GA, comparing its accuracy to other algorithms and concluding that GA is a valuable technique in the software and research industry. The report is based on a review of existing research and provides insights into the potential of GA for solving complex computational problems.

Running head: RESEARCH REPORT ON APPLICATIONS OF GENETIC ENGINEERING
Research Report on Applications of Genetic Engineering
Name of the Paper
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Table of Contents
1. Introduction..................................................................................................................................2
2. Discussion over the Presented Article.........................................................................................2
3. Conclusion...................................................................................................................................5
References........................................................................................................................................6
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1. Introduction
In the recent times, there have been major kind of improvements within the types of
approaches based on computation, which includes random, evolutionary and deterministic. This
paper puts a brief focus over the aspect of Genetic Algorithm (GA), which is considered as the
most common form of evolutionary computational technique that is majorly been used in
computing for solving different NP-Hard types of problems in relation to computation.
The following report thus discusses about the critical review on the paper focusing on the
topic based on application of genetic engineering in the field of software engineering, machine
learning and distributed computing [10]. The paper would thus discuss about the highlighted
aspects that are looked upon in the recent times and the views proposed by authors.
The selected article would be chosen based on focusing over the most discussed topics in
the field of software and research industry. Hence, the most favorable topic would be discussed
that would focus upon genetic engineering, which is the primary topic of discussion.
2. Discussion over the Presented Article
The presently discussed paper would be focusing over the wide range of applications that
are supported by genetic algorithm in the field of software engineering, machine learning and
distributed computing. The primary intention of this article is focused over understanding the
most important concepts based on GA, which can be defined as one of the most important
evolutionary technique [1]. This technique is important for solving NP-hard computational
problems. In the discussed article, different authors have put together different views based on
genetic based solutions, which are implemented by various software agencies for presenting their
own ways of research. However, the critique over the presented paper would majorly focus on
the concept of genetic algorithm, their role in genetic engineering and distributed computing and
a brief discussion on machine learning [7]. Different concepts as stated by various authors have
been put together within the article in order to understand the beneficial concepts.
In the introductory part of the article, a brief discussion is focused on understanding the
ways in which GA has been making huge amount of progress within the industry of software and
research. The article presents a strong understanding over the concept of Evolutionary
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Algorithms (EA), which had been framed on the concept of Evolution as proposed by Darwin. In
this part, the article discusses that within the concept of EA, any individual would be represented
in the form of fixed length strings known as chromosomes. It has further been described that the
computational processes based on evolutionary would make use of iterative process that includes
development or growth in the context of population. The article thus proposes many kind of
evolutionary technique such as honey bee, genetic algorithm, differential evolution, genetic
programming and many others [4]. Based on the concept framed for genetic algorithm, it can be
discussed that the classification of a problem could be made as P, NP-complete and NP-hard and
hence the classification would be dependent on the nature of complexity.
The article also presents a view based on the role of GA in the field of software
engineering, which can also be considered as one of the most vital areas of research. It is thus
also discussed as a systematic approach that is primarily been used to define the software
metrics, ensure a proper testing of software components and assure the quality of software [5]. In
the article, it has been mentioned that with the progress of technical aspects, various authors have
proposed different innovative methods to estimate costs for different software modules. The
discussion in the article also focuses over the fact that cost estimation and the related techniques
have framed a huge importance in the field of software development. In these cases, it has been
discussed that in order to estimate the cost factors, genetic algorithm is being used for generating
the initial kind of population [3]. After the application of mutation and crossover operations, the
technique of Ant Colony Optimization is mainly used for training the system in order to perform
computation based on estimating software cost and thus evaluating results.
The discussion of other authors in the article also presents innovative ways in which
genetic based solution could be used for testing the performance produced with the use of the
algorithms. From the discussion and use of the GA, it has been seen by authors that this
algorithm has been able to produce more fruitful effects in comparison to any other form of
software cost estimation models. The authors have also tested that GA has also been used for
improve software testing efficiency. In another discussion within the article, it has also been
focused that some authors have proposed a solution based on genetic that would be able to assure
the quality of software.
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The article puts emphasis on the discussion about the role of GA in the field of
distributed computing. However, in the recent times, a more focus is put on software
methodology for the purpose of achieving the end result of performed tasks. In this portion of the
article, different views of authors have been framed together that focus over the impacts made by
GA in distributed computing. This approach is being used for the purpose of solving the problem
of scheduling. The authors have identified different problems in relation to task scheduling in
various applications. They have also discussed the problem based on multiprocessor scheduling
and the impacts that could be made with a certain change of approach [12]. Hence, from the
discussion supported by the authors, the article helps in gaining a clear view over the importance
of genetic algorithmic approach, which could be used for solving different kind of complicated
problems that includes task scheduling.
The authors have focused over the development of a new kind of genetic algorithm
known as heuristic based genetic algorithm. This algorithmic technique has been used for the
purpose of scheduling of tasks in a parallel system [9]. The article presents the important aspect
based on the ways in which the algorithm would be able to minimize the time of process
completion and further increase the system throughput. The article also discusses on the fact of
distributed query optimization [11]. Various kind of researches made in the past have revealed
the fact that genetic algorithms have been used in an effective manner for optimizing the
distributed and centralized queries. These researchers have thus made a varied use of various
kind of algorithms to determine the speed of computation based on improved queries.
The article also discusses another important aspect in computing approach known as
Machine Learning. The article presents an important view based on the importance of machine
learning and data mining, which are majorly been used for extraction of meaningful information
from collected raw data [6]. Hence, this technique is majorly been used for handling information
gained from large sets of raw data. This article presents the importance of machine learning
technique and the effects made by GA within this field.
This discussion also presents the importance of artificial neural network in order to
diagnose various harmful diseases such as cancer. GA is also being used in stock market to
analyze the stock prediction and estimate the profits and risks that could be incorporated within
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the estimation of stocks. Moreover, this article also proposes an important aspect of GA in
relation to natural language processing and the ways in which this approach could be used for
understanding the kind of interaction between natural language spoken by humans and
computers [2]. Thus, based on the understanding from the article, the authors have discussed and
compared different techniques of optimization for solving word categorization [8]. The authors
have also focused over the fact that GA has a higher accuracy rate that would be similar to the
defined specific methods based on word categorization such as Viterbi.
3. Conclusion
Based on the discussion supported within the article, it has been understood that genetic
algorithm plays a major role in the recent kind of software and research industry. This article
thus presents different views based on the use of GA techniques. In the presented article, the
authors have discussed that GA has been used within various domains such as Physics,
Agriculture, Chemistry, Astronomy, Medical Science and many others. The applications based
by GA have helped in the proposing of solutions for NP-hard problems. From the discussion
supported by the authors, it can be understood that the accuracy rate for the use of genetic
algorithm is good as compared to other similar algorithms. In the further discussion over the
article, it has been understood that several efforts were made by authors for showcasing the best
possible use of genetic algorithm for solving problems in relation to domains such as Machine
Learning, Software Engineering and Database. Thus, from the review over the article, it has been
understood that GA would be proving major benefits for the purpose of solving many kind of
problems. Hence, this could be defined as a useful technique within the software and research
industry.
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References
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algorithm and particle swarm optimization. IEEE Geoscience and remote sensing letters, vol. 12,
no. 2, pp.309-313, 2014.
[4] J. Hu K. Li, K. Li and Y. Xu, A genetic algorithm for task scheduling on heterogeneous
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genetic algorithm and a DNA sequence. Optics and Lasers in Engineering, vol. 56, pp.83-93,
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[6] Z. Ghahramani, Probabilistic machine learning and artificial intelligence. Nature, vol. 521,
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sensor network based on simulated annealing and genetic algorithm. International Journal of
Hybrid Information Technology, vol. 7, no. 2, pp.71-82, 2014.
[8] J. Li, K. Gai, M. Qiu, Z. Ming and Z. Zong, Phase-change memory optimization for green
cloud with genetic algorithm. IEEE Transactions on Computers, vol. 64, no. 12, pp.3528-3540,
2015.
[9] G. Oreski and S. Oreski, Genetic algorithm-based heuristic for feature selection in credit risk
assessment. Expert systems with applications, vol. 41, no. 4, pp.2052-2064, 2014.
[10] F. Yu and X. Xu, A short-term load forecasting model of natural gas based on optimized
genetic algorithm and improved BP neural network. Applied Energy, vol. 134, pp.102-113, 2014.
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[11] J. Jose, K.C. Sekaran and T. Mathew, Study and analysis of various task scheduling
algorithms in the cloud computing environment. In 2014 International Conference on Advances
in Computing, Communications and Informatics (ICACCI), pp. 658-664, IEEE, 2014,
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[12] M.I. Jordan and T.M. Mitchell, Machine learning: Trends, perspectives, and
prospects. Science, vol. 349, no. 6245, pp.255-260, 2015.
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