What if this experiment is actually about finding a way to train cells with GAs? What I mean is, if I take the approach of simple programs and well defined limits/boundaries, is it possible for the GA to arrive to the right neighboring rules under supervised training?
Use a genetic algorithm to find the neighboring rules that make 🔬 Experiment 001a - Parsing Markdown with Cellular Automata correctly highlight markdown elements.
The layers between the raw markdown layer and the block layer in Experiment 001 are currently using hard-coded tokens values such as
TK_HEAD_SINGLE which represents a heading 1 token. But the rules by which a GA arrives to might not require such token. This means that the middle layers would have to benefit from a dynamic space that can be filled by them, although this is too vague and might not be possible to implement. Alternatively, maybe setting some middle layer generic abstraction options might make sense, such as a
HEADING_1_PREFIX which, in the training data, would be linked to the corresponding characters and block. To make this easier, the neighborhood could be increased to any number below the work-buffer provided in the program.
Just as in the previous experiment, I think there could be many solutions to this problem, finding the right one depends on predefined constraints and technical limitations.
Training data will be a markdown buffer with marked blocks.
# Heading Paragraph text goes here * list * list
So this would result in:
Line 1 -> Heading 1 (B1) Line 2 -> Empty (B1) Line 3 -> Paragraph Line (B2) Line 4 -> Paragraph Line (B2) Line 5 -> Paragraph Line (B2) Line 6 -> List item (B3) Line 7 -> List item (B3)
I'm sure there can be a more complete approach that covers all of markdown features, but this is the minimal experiment requirements.