Hello,
I can't figure out how a genetically programmed A.I. can determine when there should be a constant in the final equation. If I take the formula F(m) = ma; F(m) = m9.8, how can the A.I. know what the real number 9.8 actually is? I understand that instead of putting the final number in the binary tree, you can actually put a symbol that describes a constant and then later calculate or guess what is its value in a certain way. How would you do it?
Thank you,
Thecheeselover
I don't quite understand the question or what it has to do with genetic algorithms. GAs are just a way of gradually tweaking function parameters by employing genetics and evolutional methods. The goal is to improve the overall 'fitness' of the system.
I don't quite understand the question or what it has to do with genetic algorithms. GAs are just a way of gradually tweaking function parameters by employing genetics and evolutional methods. The goal is to improve the overall 'fitness' of the system.
If I use GP to find the quadratic formula, how an individual could find the constants? (-b +- sqrt(b² - 4ac)) / (2a) In this example, the constants are 4 and 2.
who cares about making it complex, just kill off freaks so only the most fitting survives. just make it regenerate its sense data :)
The implementation that I used for genetic programming was representing the program as a tree. Each node in the the tree had an INode interface with a method 'Evaluate( INode[] params)'
Then I had concrete implementations of nodes for each of the different operators I wanted to represent
operator+
operator*
sqrt
while(...)
if(...)
and so on...
I also had a literal operator which just returned a bounded value which was generated randomly at instantiation of the tree (through initial creation or mutation)
so at runtime I would create a concrete class through c# IL e.g.
class literal5 : INode
{
INode Execute(INode[] params)
{
return 5.0;
}
}
hope that helps
