In this blog, I will be reviewing on how I thought the Natural Computing course that I had taken at Goldsmiths University was. I will be reflecting on the topics it covered, how they were covered and what I had learned over the duration of the course. The Topics Here are the following topics, concepts […]

# Category: Natural Computing Blog

Blog posts about different aspects within natural computing. These include subjects like: swarm intelligence, genetic algorithms, ant colonisation, and more.

# Ant Colony Optimisation – An Explanation

In this blog, I will be explaining how the technique Ant Colony Optimisation works and where this can and has been applied. What is Ant Colony Optimisation? Ant Colony Optimisation (ACO) is a method that attempts to simulate how ants behave when deciding on the optimal path. This method is typically used on networking solutions, […]

# An Implementation of Genetic Algorithms

In this post I will be showing you my implementation of a problem that uses genetic algorithms as the solution. This task was created for my Natural Computing Lab on 01/12/2016. Links and Downloads If you would like to find out more about genetic algorithms, I have made a previous post about this. Please follow […]

# Genetic Algorithms

This week I will be explaining what are genetic algorithms, how they work and what role they play in the world. It is pretty interesting stuff so I highly recommend sticking around to find out more. What are Genetic Algorithms? Genetic algorithms are a form of guided random search techniques which is part of the […]

# An Application for Stochastic Diffusion Search

Following from my post last week, I’ll be talking about an example application for Stochastic Diffusion Search, otherwise known as SDS. I believe that this can be used in many different ways, but I will explain how I think it could be implemented in a search by image algorithm. If you do not know how […]

# SDS – An Explanation

What is SDS? Stochastic Diffusion Search (SDS) is a search algorithm that using swarm intelligence to achieve its outcome. The algorithm contains agents, that each have a hypothesis and a current state, and each of them communicate with each other. How does it work? In order for the swarm to find the model they are […]

# Applications for Dispersive Flies Optimisation

In the following article, I will be expanding upon the information I had given in my previous article by explaining how Dispersive Flies Optimisation can be used in a game environment such like Age of Empires. The Problem The problem that I intend DFO to solve will be to create agents for a real time […]

# Dispersive Flies Optimisation

What is Dispersive Flies Optimisation? Dispersive Flies Optimisation (DFO) is a swarm intelligence model based on how flies interact in the real world. From a paper produced by Mohammad Al-Rifaie, this has been created and used to benchmark different test functions for optimisation. The progress of the flies optimisation is based on a value called […]

# “No Free Lunch” Theorem

The “No Free Lunch” Theorem is the idea that every problem cannot be solved by a single model. This would imply that multiple models must be used/built for different particular problems. In the case of computational complexity, the “No Free Lunch” theorem states for all algorithms solving all problems that the average output would be […]