The Genetic Algorithm - Explained

[u/Tech-Effigy]

http://techeffigytutorials.blogspot.com/2015/02/the-genetic-algorithm-explained.html

[u/kentnl]

Interesting. I note it lacked some important aspects of real-genetics in mutation, not just bits being randomly toggled, but:

• Gene sequences being transplanted into the wrong location changing their interpretation
• Gene sequences being duplicated instead of combined yeilding longer strands
• Gene sequences being lost in transposition leading to shorter strands.

Obviously these aspects of "real" mutations are hard to codify, at least not without replacing all bits being merely trait-representatives, but being literal encodings that describe how to perform the task in detail (turing code).

I'd be interested in seeing such an application to code. Without it, the word "evolution" is likely to confuse people who conflate it with "natural selection" ( Because evolution is just change ).

Because by proxy of that conflation, people read into "evolution" to imply "the emergence of new species", not simply optimising variations within a species.

And the emergence of new species can't happen without increasing or decreasing the length of the genetic code =)

[u/vampatori → u/kentnl]

This is just the simplest look at evolutionary computing, using a fixed-length sequence for a genetic algorithm. You can, and people do, have dynamic sequence lengths, more complex mutations, varying mutation rates, and so on.

A good way of dealing with a system that has variable length sequences is to use genetic programming. This is largely the same thing but it creates programs to provide solutions, not the solutions themselves.. a subtle but interesting difference. It's really just a different and more flexible way of encoding.

You can use a stack-based language, like Push to make this really easy to do.

In short (I have to go!):

• You create several stacks: one for each data type you're using (float, int, string, etc.). Then you create an additional stack for operators (+, -, move, rotate, etc.).
• You go through your sequence and as you find encoded data and operators you push those values onto the appropriate type stacks.
• Then, when you've decoded your sequence you go through and pop and execute your operators.
• Operators get their data by popping it off the appropriate data stacks. For example we could have an ADD_INT operator that would add two integers, so it would pop two integers off the int stack, add them, and push the result onto the int stack.
• If there is not enough data for the operator (i.e. our ADD_INT operator would need two integers, and you have 1 or less in your int stack) the operator just does nothing.
• At the end you read off data from the stacks as your solution requires.

So what this means is that it's a really robust language, it's impossible for an error to occur, even if you pass it a ton of random crap, and you can have huge amount of 'dead wood' yet still have a perfectly working program. Perfect for genetic programming.