Background Page: Genetic Algorithms


Ever since Darwin, we've had a fairly good idea of how the complexity of the world's organisms has appeared. It's the result of a stochastic optimisation process known as natural selection, which turns out to be very good at producing efficient solutions to problems.

This raises an interesting question: can we harness this effect to produce better designs? What problems is this approach good at solving? How do we tailor the approach to be as effective as possible? These questions are encapsulated and answered in the study of genetic algorithms.

This isn't strictly a part of computational biology, but it's a technique that's widely used in this field (if only because CBists have generally come across GAs). See, for example, this paper.

Problem solved

How can we use evolutionary processes to solve optimisation problems?


"Evolutionary Computing" - Kenneth A. De Jong. Barely started reading.


None so far.


Blogger Francois Rivest said...

There is also something more ambitous than simple genetic algorithms called Genetic Programming. Try to look for John Koza works. I am not sure if he is still around, but he is somehwat the father of this sub-field.

7/24/2006 3:43 pm  
Blogger Francois Rivest said...

Here's a link with some online tutorial on genetic programming: http://www.genalgo.com/index.php?option=com_content&task=view&id=74&Itemid=30

7/25/2006 3:14 am  
Blogger Coalescent said...

Thanks, I was hoping to cover that sort of topic. Didn't realise it was actually referred to by a different name, so you just saved me much fruitless searching :)

7/28/2006 10:42 am  

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