Posts Tagged genetic algorithms
Genetic Algorithm Examples
Posted by Howard Yeend in machine learning on March 3, 2011
There’s been a lot of buzz recently on reddit and HN about genetic algorithms. Some impressive new demos have surfaced and I’d like to take this opportunity to review some of the cool things people have done with genetic algorithms, a fascinating subfield of evolutionary computing / machine learning (which is itself a part of the broader study of artificial intelligence (ah how academics love to classify things (and nest parentheses (especially computer scientists)))).
If you’re new to genetic algorithms, don’t be put off by the awesome/scary name – you might like to check out my own modest contribution to the field of abusing science to make cool useless things; the hello world genetic algorithm tutorial. It’s written as a short genetic algorithm tutorial to help beginners understand genetic algorithms from the ground up (and it has a pretty interactive demo in JavaScript too!).
So here’s my top five evolutionary scripts:
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Genetic Algorithm For Hello World
Posted by Howard Yeend in javascript, machine learning on December 13, 2010
This article works through the creation of a ‘toy’ genetic algorithm which starts with a few hundred random strings and evolves towards the phrase “Hello World!”. It’s a toy example because we know in advance what the optimum solution is – the phrase “Hello World!” – but it provides a nice simple introduction to evolutionary algorithms.
I have written this article primarily for developers who have a casual interest in machine learning. I don’t talk much about the implementation of the code itself because there’s not much of interest there – the beauty of genetic algorithms is their simplicity, so the code isn’t that interesting, other than in as much as it’s not usual to do such things in JavaScript. For ‘real’ applications of genetic algorithms, I’d suggest looking into existing established frameworks for your language.
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A quick overview of machine learning techniques
Posted by Howard Yeend in machine learning on November 27, 2010
Machine learning is a fascinating discipline. Often inspired by natural processes, it can produce astounding results in a wide range of applications. Modern web search is underpinned by ML techniques such as clustering and statistical text processing. Computer games make use of evolutionary algorithms to produce better artificial enemies. Your camera probably has face detection in it for aiding auto-focus. Machine learning is key to making our technology better and our lives easier.
Today I’m going to give a very brief and incomplete overview of machine learning technologies and applications. There are three broad types of machine learning: Categorisation, Optimisation and Prediction.
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