Data object creation and recommendation using machine learning based online evolution
Abstract:
The technology disclosed relates to providing expanded data object correlations for user-generated web customizations. Roughly described, it relates to creating an initial plurality of candidate data object image representations by procreation, storing a candidate population having the plurality of candidate data object image representations, scoring each of the candidate data object image representations for conformity with a predefined goal using a trained neural network system and/or a rule-based percept filter, discarding candidate data object image representations from the candidate population, adding new data object image representations to the candidate population by procreation, iterating the scoring, the discarding, and the adding until the discarding yields a candidate pool of candidate data object image representations not yet discarded but which satisfy a convergence condition, and providing as recommendation for display to a user at least one of the data object images represented in the candidate pool.
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