You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The Distance function compares row-wise for each element to see if they match. However, how does this reflect to AGraphs with the same equation tree, but with different command arrays. For example let,
And distance(command_array_1, command_array_2) = 8 since the first row is the same, and the 0 in the 1st column-index of the 2nd row-index is in both arrays. However they both describe an equation f(x) = c_0 * x_0. Should the distance reflect the AGraphs or the command arrays?
The text was updated successfully, but these errors were encountered:
Ideally distance is a measure of dissimilarity between the AGraphs. It was implemented in the current way mainly based on a need for fast evaluation. We could try a different (and likely slower) implementation and see if the reduction in evaluation speed is made up in quicker evolutionary convergence.
The Distance function compares row-wise for each element to see if they match. However, how does this reflect to AGraphs with the same equation tree, but with different command arrays. For example let,
and
And
distance(command_array_1, command_array_2) = 8
since the first row is the same, and the 0 in the 1st column-index of the 2nd row-index is in both arrays. However they both describe an equationf(x) = c_0 * x_0
. Should the distance reflect the AGraphs or the command arrays?The text was updated successfully, but these errors were encountered: