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License: MIT License
How many weights would be needed?
If we use the number of generations for weight array size, how many more evaluations would be required?
No heuristics needed for individuals with different numbers of weights that influences the fitness
How many calculations would it save on slightly lower numbers?
Model cost / advantage of this method
Saving a confusion matrix for each iteration blows up, if there are many generations.
Either compute metrics and cache those or just evaluate the mean confusion matrix.
Relevant questions here:
Should rewann be adpated to use sklearn? Check out sklearn-genetic and compare pros and cons
The amount of code to maintain should be reduced. Lean on standard libs where possible. Remove unnecessary bloaty concepts.
Check-out:
sklearn-genetic
Store:
Mglw. weniger relevant, sobald negative Gewichte zugelassen sind.
Performance metrics will be influenced by the introduction of multiple tests results for randomly sampled weights ( #25 ).
Peformance module should:
Update experiment defaults with rewann_default.toml -> this allows for environment specific default values (useful for debug, storage, and num_workers settings).
Knoten, die schon lange bestehn, eher verbinden (possibility matrix multiplizieren)
Individual is a representation for networks. Networks can be in different states, and not all states should be constantly in memory. To still be able to easily be able to work with each possible expression of the network (genotype, phenotype, etc.) translations should implicitly be made.
Where suitable, certain expressions will be saved to the file system for later inspection.
Enable loading & saving of all representations from & to FS
Translate expressions, save all translated versions
Evaluate Individual on a given task (possibly using certain seed / other parameters)
Sprechnen Sie uns an, wenn Sie lange Rechnungen aufsetzen, die nicht unterbrechbar sind. Rechnerbetriebsgruppe
Originally posted by @plonerma in #2 (comment)
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