Cooperative execution of a genetic algorithm with an efficient training algorithm for data-driven model creation
First Claim
1. A computer system comprising:
- a memory configured to store an input data set and a plurality of data structures, each of the plurality of data structures including data representative of a neural network;
a processor configured to execute a recursive search, wherein executing the recursive search comprises, during a first iteration;
determining a fitness value for each of the plurality of data structures based on at least a subset of the input data set;
selecting a subset of data structures from the plurality of data structures based on the fitness values of the subset of data structures;
performing at least one of a crossover operation or a mutation operation with respect to at least one data structure of the subset to generate a trainable data structure; and
providing the trainable data structure to an optimization trainer, the optimization trainer configured to;
train the trainable data structure based on a portion of the input data set to generate a trained data structure; and
provide the trained data structure as input to a second iteration of the recursive search that is subsequent to the first iteration.
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Abstract
A method includes, based on a fitness function, selecting a subset of models from a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model and sending the trainable model to an optimization trainer. The method includes adding a trained model received from the optimization trainer as input to a second epoch of the genetic algorithm that is subsequent to the first epoch.
48 Citations
20 Claims
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1. A computer system comprising:
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a memory configured to store an input data set and a plurality of data structures, each of the plurality of data structures including data representative of a neural network; a processor configured to execute a recursive search, wherein executing the recursive search comprises, during a first iteration; determining a fitness value for each of the plurality of data structures based on at least a subset of the input data set; selecting a subset of data structures from the plurality of data structures based on the fitness values of the subset of data structures; performing at least one of a crossover operation or a mutation operation with respect to at least one data structure of the subset to generate a trainable data structure; and providing the trainable data structure to an optimization trainer, the optimization trainer configured to; train the trainable data structure based on a portion of the input data set to generate a trained data structure; and provide the trained data structure as input to a second iteration of the recursive search that is subsequent to the first iteration. - View Dependent Claims (2)
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3. A method comprising:
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based on a fitness function, selecting, by a processor of a computing device, a subset of models from a plurality of models, the plurality of models generated based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm, wherein each of the plurality of models includes data representative of a neural network; performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model; sending the trainable model to an optimization trainer; and adding a trained model received from the optimization trainer as input to a second epoch of the genetic algorithm that is subsequent to the first epoch. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A non-transitory computer-readable storage device storing instructions that, when executed, cause a computer to perform operations comprising:
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based on a fitness function, selecting a subset of models from a plurality of models, the plurality of models generated based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm, wherein each of the plurality of models includes data representative of a neural network; performing at least one genetic operation of the genetic algorithm with respect to at least one model of the subset to generate a trainable model; sending the trainable model to a trainer; and adding a trained model received from the trainer as input to a second epoch of the genetic algorithm that is subsequent to the first epoch. - View Dependent Claims (20)
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Specification