given a fitness measure, implement GA and other algorithms to continuously find associations of measures and entities, i.e. for generic pattern recognition.
An output of the GA (probably the gene directly) might be,
for example, "messages sent between 23h-4h are more incident
from intermediary paricipants and hubs, but not the few most active hubs."
or the already found:
"the ~20 participants with greatest betweenness make almost perfectly disjunct group
with the ~20 participants with greatest closenness".
or
"Intermediary participants with high out degree use shorter words, longer words otherwise"
"the set of participants with greatest clustering and degree above 3
have local vocabulary and linguistic structure".
etc.
given a fitness measure, implement GA and other algorithms to continuously find associations of measures and entities, i.e. for generic pattern recognition.
An output of the GA (probably the gene directly) might be,
for example, "messages sent between 23h-4h are more incident
from intermediary paricipants and hubs, but not the few most active hubs."
or the already found:
"the ~20 participants with greatest betweenness make almost perfectly disjunct group
with the ~20 participants with greatest closenness".
or
"Intermediary participants with high out degree use shorter words, longer words otherwise"
"the set of participants with greatest clustering and degree above 3
have local vocabulary and linguistic structure".
etc.