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Category Archives: Natrual Algorithms
AirBnB Preprocessing
Before I was able to start on the application of Dispersive Flies Optimisation to the AirBnB data, it occured to me that while I could use the information as it was, things would be much simpler further down the line … Continue reading
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GA: Analysis to Implementation
Scenario: Assume that your GA has chromosomes in the following structure: ch = (g0, g1, g2, g3, g4, g5, g6, g7) g07 can be any digits between zero to nine. The fitness of each chromosome is calculated using the following … Continue reading
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Steady State vs Modular Genetic Algorithms
Once again with definitions and stuff. I’m sure this makes for an absolutely thrilling read. Below we’re talk about the different types of genetic algorithm. Pretty straight forward.
An Introduction to Genetic Algorithms
Genetic algorithms are a form of evolutionary computation pioneered by one John Henry Holland in 1975. At the time, the main limitation of applying early genetic algorithms was computing power. Because apparently my current computer is like 30,000 times more powerful than my … Continue reading
Fitness Evaluation
This week on Natural Algorithms: We learn some terminology, Kriss makes a wisecrack and a dog does science! Okay, so the point here is that we’re looking at different types of fitness functions. We divide the type of function an algorithm … Continue reading
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Algorithm: Stochastic Diffusion Search
Stochastic Diffusion Search is a search algorithm that can take the form of either a neural network or a swarm and attempts an optimal application of resources. The agents scatter randomly across the search area and keep searching random locations … Continue reading
A Potential Application of Dispersive Flies Optimisation
In this post, we’ll be exploring the application of Dispersive Flies Optimisation, as originally pondered in my previous post. Specifically, we’ll discuss applying DFO to AirBnB data, as the AirBnB data is readily available with very little effort. I will … Continue reading
An Introduction to Dispersive Flies Optimisation
It’s quite curious that at the time of writing, this algorithm doesn’t have so much as a Wikipedia page. Heck, a cursory Google search implies that the individual who came up with it is the very same guy who asked … Continue reading
The No Free Lunch Theorem
There are two dominant No Free Lunch theorem’s that relate to computing. One is focused towards search and optimisation, while the other is for supervised machine learning. The Key Point Eric Cai eloquently describes the No Free Lunch theorem in … Continue reading
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