Examples of Heuristics in Computer Science

This image shows how heuristics impact everyday life and computer science.

The post Examples of Heuristics in Computer Science first appeared on Qvault.

Heuristics in computer science and artificial intelligence are “rules of thumb” that are used in algorithms to assist in finding approximate solutions for complex problems. Often, there is simply too much data to sift through in order to come to a solution in a timely manner, and so, a heuristic algorithm is used to trade exactness for speed. However, because heuristics are based on individual rules unique to the problem they are solving, the specifics of the heuristics vary from problem to problem. Example problems and their heuristics are given below.

Traveling Salesperson Problem (TSP)

The TSP is a famous algorithm with a Big-O of O(n!) and asks the question:

Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?

For a low number of cities, this question could be reasonably brute-forced. However, as the number of cities increases, it becomes increasingly difficult to come to a solution.

The nearest-neighbor (NN) heuristic solves this problem nicely: the computer always picks the nearest unvisited city next on the path. NN does not always provide the best solution, but it is close enough to the best solution that the difference is often negligible for the purpose of answering the TSP. By using this heuristic, the Big-O complexity of TSP can be reduced from O(n!) to O(n^2).

Knapsack Problem

The knapsack problem poses the issue:

Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.

An example heuristic for this problem is a greedy algorithm, which sorts the items in descending order of value per weight, and then proceeds to insert them into the “sack”. This ensures the most valuably “dense” items make it into the sack first.

Search Optimization

Search engine optimization has been sought after for as long as search engines have been around. Individuals using search engines want to find the information they are looking for as swiftly as possible. With such an incredible amount of information available, search engines must utilize heuristics in order to expedite the search process. At the start, a heuristic could try each possibility at each step, but as the search continues, it can choose to stop the search at any time if the current possibility is worse than the best solution already located. In this way, the search engine can be optimized for speed and correctness.

Applying Heuristics to Your Algorithms

In order to apply heuristics to your algorithms, you need to know the solution or goal you’re looking for ahead of time. If you know your end goal, you can specify rules that can help you achieve it. If the algorithm is being designed to find out how many moves a knight can make on a square, 8×8 chessboard while visiting every square, it’s possible to create a heuristic that causes the knight to always choose the path with the most available moves afterward. However, because we’re trying to create a specific path, it may be better to create a heuristic that causes the knight to choose the path with the fewest available moves afterward. Since the available decisions are much narrower, so too are the available solutions, and so they are found more quickly.

Thanks For Reading!

Take computer science courses on our new platform

Follow and hit us up on Twitter @q_vault if you have any questions or comments

Subscribe to our Newsletter for more programming articles



source https://qvault.io/2020/11/30/examples-of-heuristics-in-computer-science/

Comments

Post a Comment

Popular posts from this blog

Why is Exclusive Or (XOR) Important in Cryptography?

Image
If you are getting into cryptography, or just trying to understand the fundamentals, you may have noticed that the exclusive-or operation is used quite often, especially in ciphers. What is XOR ( ⊕ )? XOR, or “exclusive or” operates on binary data. It returns true if both of its inputs are opposites (one false and one true), otherwise, it returns false. An example in go code would be something like: func exclusiveOr(a bool, b bool) bool { return a != b } XOR Cipher – The Perfect Cipher The XOR operation can be used as a simple cipher for encrypting and decrypting messages with a single key. This is known as symmetric encryption. It is interesting to note that if: The key is the same size as the message The key is kept secret and generated truly randomly Then the cipher is impossible to crack. This is known as a one time pad . However, a simple XOR shouldn’t be used in production due to the key length needing to be too long to be practical. Cipher Example A...
Read more

Base64 vs Base58 Encoding

Image
By Lane Wagner –  @wagslane  on Twitter Base64 is one of the most popular encoding formats for representing data. Have some binary data? Base64 encode it for convenient readability and parsing. Base58 is just another encoding format (with 58 characters instead of 64, and has gained popularity largely due to Bitcoin and other cryptocurrencies. When it comes to data encoding, there is typically a trade-off made between: Human Readability: Do humans have a good idea of what is being represented at a glance? Efficient data compression: How many bytes are used to represent the same data? Let’s rank Base58, Base64, and ASCII encoding against each other using these metrics! First: A Note on Binary All data is stored in a raw binary format on computers. These encoding formats (Base64, Base58, and ASCII) are just different ways of reading and writing binary data. For example, in Base64 the binary code 000000 represents the letter A , but in ASCII the binary 00000000 repres...
Read more

(Very) Basic Intro to Hash Functions (SHA-256, MD-5, etc)

Image
This is going to be a basic introduction to hash functions. I will assume most of my audience is here to gain an understanding of why hash functions are used and the basic idea of why they work. My goal is to explain it in a general sense, I will be omitting proofs and implementation details and instead focusing on the high-level principles. Why Use A Hash Function? Hash functions are used all over the internet in order to securely store passwords, find duplicate records, quickly store and retrieve data, and more. For example,  Qvault  uses hashes to extend master passwords into private encryption keys. A longer list of uses here:  https://en.wikipedia.org/wiki/Hash_function#Uses I want to focus on several important features of hash functions, arguably the most important features. Hash functions scramble data deterministically No matter the input, the output of a hash function always has the same size The original data can not be retrieved from the scrambled d...
Read more