US 11,853,893 B2
Execution of a genetic algorithm having variable epoch size with selective execution of a training algorithm
Sari Andoni, Austin, TX (US); Keith D. Moore, Cedar Park, TX (US); Elmira M. Bonab, Austin, TX (US); and Junhwan Choi, Austin, TX (US)
Assigned to SPARKCOGNITION, INC., Austin, TX (US)
Filed by SparkCognition, Inc., Austin, TX (US)
Filed on Jun. 1, 2021, as Appl. No. 17/335,954.
Application 17/335,954 is a continuation of application No. 15/697,158, filed on Sep. 6, 2017, granted, now 11,074,503.
Prior Publication US 2021/0287097 A1, Sep. 16, 2021
Int. Cl. G06N 3/08 (2023.01); G06N 3/12 (2023.01); G06N 3/084 (2023.01); G06N 3/126 (2023.01)
CPC G06N 3/084 (2013.01) [G06N 3/126 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a trained model from a trainer, the trained model representing a trained version of a first model of a plurality of models of a first epoch of a genetic algorithm, wherein each of the plurality of models includes data representative of a neural network;
generating an input set of models for a second epoch of the genetic algorithm, the input set of models including the trained model and a second model of the plurality of models of the first epoch, wherein the second epoch is subsequent to the first epoch, and wherein the second model is not trained between the first epoch and the second epoch; and
generating, after the second epoch of the genetic algorithm, an output set of models, the output set of models based at least in part on modifying one or more characteristics of the input set of models of the second epoch.