首页> 外国专利> Cooperative execution of a genetic algorithm with an efficient training algorithm for data-driven model creation

Cooperative execution of a genetic algorithm with an efficient training algorithm for data-driven model creation

机译:遗传算法与有效训练算法的协同执行,用于数据驱动的模型创建

摘要

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.
机译:一种方法包括基于适应度函数从多个模型中选择模型的子集。多个模型是基于遗传算法生成的,并且对应于遗传算法的第一时期。多个模型中的每个模型都包括代表神经网络的数据。该方法还包括相对于子集的至少一个模型执行遗传算法的至少一个遗传运算,以生成可训练模型并将该可训练模型发送给优化训练器。该方法包括将从优化训练器接收的训练后的模型作为输入添加到遗传算法的第二时期,该第二时期在第一时期之后。

著录项

  • 公开/公告号US9785886B1

    专利类型

  • 公开/公告日2017-10-10

    原文格式PDF

  • 申请/专利权人 SPARKCOGNITION INC.;

    申请/专利号US201715489564

  • 申请日2017-04-17

  • 分类号G06F15/18;G06N3;G06N3/12;G06N3/08;G06F17/30;G06N3/04;

  • 国家 US

  • 入库时间 2022-08-21 13:43:30

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