Genetic Algorithms are the fundamental building blocks of computer programming. They’re found in all sorts of software, from search engines to social media networks. While there are many different genetic algorithms, some are more common than others. In this blog post, we’ll take a look at the ten most popular genetic algorithms and explain what they do. We’ll also discuss how these algorithms are used in software testing and provide some examples. By the end of this post, you’ll have a better understanding of how genetic algorithms work and why they’re so important for software development.
Searching and Sorting
The first type of genetic algorithm is searching and sorting. This includes the algorithms that allow your computer to search for a piece of data like a phone number or another document and the algorithms that determine how data should be ordered by sorting it into alphabetical order or chronological order. One common example of a searching and sorting algorithm is the binary search.
Binary search algorithms are used to find specific data quickly in large lists of data. For example, if you were looking through a phone book with 100 pages containing 100 names per page, it would take around 50 steps to go through all of the names using a linear search algorithm. However, one would only need to go through 10 steps with a binary search algorithm.
Next, graph algorithms are used with networks and data structures such as trees and lists. For example, the most common type of graph is a tree, which you might recognize from file directory listings or outlines in school textbooks. A way to visualize a tree is like a family tree.
Graphs are used in software testing to verify that the data on your computer (such as file access privileges) follows the proper connections between different pieces of data stored on the hard drive, such as files and folders.
For example, if you delete one folder with test cases for an application still leaves folders and files behind. If the folder and files had proper access privileges, this would be a problem; graph algorithms can check to ensure it doesn’t happen.
String algorithms are used with strings of characters like words or names. They’re very important for cryptography, which is the science of encrypting and decrypting data. Cryptography is used in software testing, for example, to encrypt passwords so that a cyber-criminal can’t hack them.
String algorithms are also important for data compression algorithms, which save space on your hard drive or another type of storage device. You’ve probably been using compression without even realizing it: ZIP files are one of the most common types of compressed data. However, software test engineers use many other types of compression algorithms for specific purposes. For example, the HFS+ file compression algorithm is specially designed for Apple operating systems to save space on Mac hard drives.
Linear programming (LP) is an algorithm designed to minimize or maximize a linear function with two variables. In other words, it allows you to find the lowest price for a product by testing different values of the ingredients used to make that product. For example, let’s say you’re manufacturing brownies and want to use as many chocolate chips as possible without going over your budget. You could use linear programming to determine exactly how many chocolate chips you should use to make the most brownies possible without going over your budget (and using as few of the other ingredients) as possible.
Linear programming is also used by engineers who need to optimize their products. This allows them to test different variables for a product and find the best one. For example, suppose you’re designing a product before it goes into production. In that case, you can use linear programming to determine the best price for your product and the most efficient way to make it.
Network Flow Algorithms
The last types of genetic algorithms are network flow algorithms, which themselves have three subcategories:
Communication flow algorithms are used to find the most efficient way for different data types to travel through a network. For example, let’s say you have a factory with 1,000 computers and want to send 1 million bits of information from one computer in the middle to another computer at the end of the network in five days. Network flow would help you find the most efficient way to send that information.
Flow-based network algorithms are similar to communication flow algorithms but are used with networks where a constant amount of data is sent, rather than just one piece. So, for example, let’s say every day at 8 p.m., you have 200 orders for your products arrive, and you want to make sure all of them get routed through your database as quickly as possible. So you could use flow-based network algorithms to determine the best way to route those 200 orders at 8 p.m., based on all the items in your database and the number of orders that need to be processed.
Algorithms are the building blocks of computer programming. Without them, computers would not be nearly as useful because they wouldn’t be able to make decisions based on the data you provide them. Algorithms are so important that without any software test engineers around to develop new ones, we’d soon run out of uses for our computers and other digital devices.