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Title: ML-GA

Abstract

ML-GA is a software that can be used to expedite design optimization process combining machine learning and genetic algorithm approaches. It employs a machine learning (ML) model (any ML algorithm can be incorporated) to predict the quality (merit) of a design from the input parameters. Then, a stochastic global optimization genetic algorithm (GA) is used, with the machine learning model as the objective function, to optimize the input parameters based on the merit function. ML-GA is scalable to higher computational platforms like supercomputers and clusters enabling optimization to be performed in significantly short time frames (e.g., in a day).

Authors:
 [1];  [2]; ;  [2]
  1. UChicago Argonne
  2. UCHICAGO ARGONNE, LLC
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
Contributing Org.:
UCHICAGO ARGONNE, LLC
OSTI Identifier:
1489385
Report Number(s):
ML-GA; 005827MLTPL00
ANL-SF-18-098
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Software
Software Revision:
00
Software Package Number:
005827
Software CPU:
MLTPL
Source Code Available:
No
Country of Publication:
United States

Citation Formats

PAL, PINAKI, MOIZ, AHMED ABDUL, KODAVASAL, JANARDHAN, and SOM, SIBENDU. ML-GA. Computer software. Vers. 00. USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V). 8 Nov. 2017. Web.
PAL, PINAKI, MOIZ, AHMED ABDUL, KODAVASAL, JANARDHAN, & SOM, SIBENDU. (2017, November 8). ML-GA (Version 00) [Computer software].
PAL, PINAKI, MOIZ, AHMED ABDUL, KODAVASAL, JANARDHAN, and SOM, SIBENDU. ML-GA. Computer software. Version 00. November 8, 2017.
@misc{osti_1489385,
title = {ML-GA, Version 00},
author = {PAL, PINAKI and MOIZ, AHMED ABDUL and KODAVASAL, JANARDHAN and SOM, SIBENDU},
abstractNote = {ML-GA is a software that can be used to expedite design optimization process combining machine learning and genetic algorithm approaches. It employs a machine learning (ML) model (any ML algorithm can be incorporated) to predict the quality (merit) of a design from the input parameters. Then, a stochastic global optimization genetic algorithm (GA) is used, with the machine learning model as the objective function, to optimize the input parameters based on the merit function. ML-GA is scalable to higher computational platforms like supercomputers and clusters enabling optimization to be performed in significantly short time frames (e.g., in a day).},
doi = {},
year = {2017},
month = {11},
note =
}

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