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PARALLELIZATION APPROACHES OF MODIFIED TEACHING LEARNING BASED SEARCH OPTIMIZATION TECHNIQUE FOR VARIABLE SELECTION

机译:基于变量选择的搜索优化技术的并行化学习方法

摘要

Systems and methods include initializing a trainees population (TP), calculating an objective function (OF) of the TP to identify a trainer. A teaching pool is created using variables of each trainee and the identified trainer, and unique variables are added to obtain an updated teaching pool (UTP), a search is performed in parallel on UTPs to obtain ˜m™ subset of variables and OFs. OFs of ˜m™ subset are compared with OFs of the trainee™s and variables of a first trainee in each thread are updated accordingly. In parallel, an updated learning pool (ULP) is created for selected trainee and the trainees, by adding unique variables to obtain ˜n™ subset which are compared with objective functions of selected trainee and the trainees and variables of a second trainee are updated accordingly. These steps are iteratively performed to obtain an optimal subset of variables that is selected for teaching and learning phase.
机译:系统和方法包括初始化受训者人数(TP),计算TP的目标函数(OF)以识别培训者。使用每个受训者和所标识的培训者的变量创建教学池,并添加唯一变量以获得更新的教学池(UTP),对UTP并行执行搜索以获得变量和OF的〜m™子集。将〜m TM子集的OF与受训者TM的OF进行比较,并且相应地更新每个线程中的第一受训者的变量。并行地,通过添加唯一变量以获得〜n TM子集来为选定的受训者和受训者创建更新的学习池(ULP),将其与选定受训者的目标函数进行比较,并且相应地更新第二受训者的变量。 。重复执行这些步骤,以获得选择用于教学阶段的变量的最佳子集。

著录项

  • 公开/公告号IN201621039514A

    专利类型

  • 公开/公告日2018-05-25

    原文格式PDF

  • 申请/专利权人

    申请/专利号IN201621039514

  • 发明设计人 RAMAMURTHI NARAYANAN;KONETI GEERVANI;

    申请日2016-11-19

  • 分类号G06N5/02;G06F15/18;

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