Author Archives: AMGitsKriss
If you’re anything like me, you recognise the need for conflict to drive narrative and character development, but at times have difficulty putting two and two together. This can be especially apparent when it comes to tabletop RPG’s where you … Continue reading
Office Hours is a weekly rollplaying Q&A show hosted by Adam Koebel. Adam is an author/co-designed of the game Dungeon World, and the resident Dungeon Master for both Roll20.net and RollPlay. The complete playlist can be viewed on YouTube here, … Continue reading
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
Scenario: Assume that your GA has chromosomes in the following structure: ch = (g0, g1, g2, g3, g4, g5, g6, g7) g0-7 can be any digits between zero to nine. The fitness of each chromosome is calculated using the following … Continue reading
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.
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
It’s been a thousand years since the Great Exodus and little fewer since the Age of Dragons came to an end. The horrors of The Old World have long since been forgotten. Civilisation thrives in the new land of Epimia.
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
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
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