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https://github.com/mysftz/applications-of-evolutionary-algorithms-in-bioinformatics-report

A LaTeX research document on applications of evolutionary algorithms in bioinformatics.
https://github.com/mysftz/applications-of-evolutionary-algorithms-in-bioinformatics-report

algorithm algorithms bioinformatics computer-science evolutionary-algorithms latex latex-document university university-coursework

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A LaTeX research document on applications of evolutionary algorithms in bioinformatics.

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# Applications of Evolutionary Algorithms in Bioinformatics Report
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Table of Contents



  1. About The Project


  2. Usage

  3. License

  4. Other Projects

  5. Contact

## About The Project
### Introduction

Evolutionary algorithms are used to provide approximated solutions to a variety of problems by assigning a function to each individual component and rating those individual components by a set of chosen variables. In Bioinformatics, this takes the form of the genetic algorithm. This algorithm specifies individuals or chromosomes that correspond to binary strings to which an operator is applied and manipulates the string, from parent to children, this repeats until a set number of iterations have passed or a termination condition is met. The simplest idea is that of single-point crossover in which two parents split their sequence to share with their children where as the mutation operation can occur in which a binary string will change values upon crossover. Specifically in Bioinformatics and Biology, "In the medical field GA-based solutions have been posed for a variety of problems including symptom and ailment classification, visualization as well as identification and diagnoses of diseases.", thus proving benefits in using evolutionary algorithms in this application.

In conclusion, this essay highlights the structure and the use of the application of evolutionary algorithms (specifically the genetic algorithm) in Bioinformatics, while still controversially used proves to be highly useful in examining large amounts of data which is vastly apparent in Bioinformatics and analyzing the data via a variety of pre-chosen variables. Only used in a few areas within bioinformatics and biology, there is still more uses for these evolutionary algorithms as they have proven to provide solutions based off the strength of its own data that fit the chosen variables, it can be said that these algorithms have a very broad reach in every area of research and experimental testing. It could be said that with an expanse of computational bioinformatics, the use of evolutionary algorithms may rise as the disadvantages lack trepidation.

### Built With

LaTeX, Overleaf, BibTeX

### Other Infomation

LaTeX Report: https://github.com/Mysftz/applications-of-evolutionary-algorithms-in-bioinformatics-report

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## Usage

This report was submitted for the degree of a Masters of Science in Computer Science (Artificial Intelligence) at the University of Kent in December 2021.

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## License
Distributed under the CC-BY-SA-4.0: Creative Commons Attribution Share Alike 4.0 International License. See `LICENSE.txt` for more information.

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## Contact

GitHub: [@Mysftz](https://github.com/Mysftz)  ·  Portfolio: [Website](https://mysftz.github.io)  ·  LinkedIn: [@lrgtomaszewski](https://www.linkedin.com/in/lrgtomaszewski/)  ·  Instagram: [@Mysftz](https://www.instagram.com/mysftz/)  ·  Twitter: [@MysftzUK](https://twitter.com/MysftzUK)

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