skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Classifier-Guided Sampling Optimizer v. 1.0

Abstract

The Classifier-Guided Sampling (CGS) Optimizer is a software implementation of the CGS algorithm, a type of evolutionary algorithm for performing search and optimization over a set of discrete design variables in the face of one or more objective functions.

Authors:
 [1]
  1. Sandia National Laboratories
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1351602
Report Number(s):
CGS Optimizer; 005218MLTPL00
SCR #2189
DOE Contract Number:
AC04-94AL85000
Resource Type:
Software
Software Revision:
00
Software Package Number:
005218
Software CPU:
MLTPL
Open Source:
Yes
Source Code Available:
Yes
Country of Publication:
United States

Citation Formats

Backlund, Peter B. Classifier-Guided Sampling Optimizer v. 1.0. Computer software. https://www.osti.gov//servlets/purl/1351602. Vers. 00. USDOE. 14 Mar. 2017. Web.
Backlund, Peter B. (2017, March 14). Classifier-Guided Sampling Optimizer v. 1.0 (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1351602.
Backlund, Peter B. Classifier-Guided Sampling Optimizer v. 1.0. Computer software. Version 00. March 14, 2017. https://www.osti.gov//servlets/purl/1351602.
@misc{osti_1351602,
title = {Classifier-Guided Sampling Optimizer v. 1.0, Version 00},
author = {Backlund, Peter B.},
abstractNote = {The Classifier-Guided Sampling (CGS) Optimizer is a software implementation of the CGS algorithm, a type of evolutionary algorithm for performing search and optimization over a set of discrete design variables in the face of one or more objective functions.},
url = {https://www.osti.gov//servlets/purl/1351602},
doi = {},
year = {Tue Mar 14 00:00:00 EDT 2017},
month = {Tue Mar 14 00:00:00 EDT 2017},
note =
}

Software:
To order this software, request consultation services, or receive further information, please fill out the following request.

Save / Share:
  • Abstract not provided.
  • This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of omore » bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.« less
  • Abstract not provided.
  • A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS method uses a Bayesian network classifier for predicting the performance of new designs based on a set of known observations or training points. Unlike most metamodeling techniques, however, the classifier assigns a categorical class label to a new design, rather than predicting the resulting response in continuous space, and thereby accommodates nondifferentiable and discontinuous functions of discrete or categorical variables. The CGS method usesmore » these classifiers to guide a population-based sampling process towards combinations of discrete and/or continuous variable values with a high probability of yielding preferred performance. Accordingly, the CGS method is appropriate for discrete/discontinuous design problems that are ill-suited for conventional metamodeling techniques and too computationally expensive to be solved by population-based algorithms alone. In addition, the rates of convergence and computational properties of the CGS method are investigated when applied to a set of discrete variable optimization problems. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms.« less
  • Genomic islands are key mobile DNA elements in bacterial evolution, that can distinguish pathogenic strains from each other, or distinguish pathogenic strains from non-pathogenic strains. Their detection in genomes is a challenging problem. We present 3 main software components that attack the island detection problem on two different bases: 1) the preference of islands to insert in chromosomal tRNA or tmRNA genes (islander.pl), and 2) islands' sporadic occurrence among closely related strains. The latter principle is employed in both an algorithm (learnedPhyloblocks.pl) and a visualization method (panGenome.pl). Component islander.pl finds islands based on their preference for a particular target genemore » type. We annotate each tRNA and tmRNA gene, find fragments of each such gene as candidates for the distal ends of islands, and filter candidates to remove false positives. Component learnedPhyloblocks.pl uses islands found by islander.pl and other methods as a training set to find new islands. Reference genomes are aligned using mugsy, then the "phylotypes" or patterns of occurrence in the reference set are determined for each position in the target genome, and those phylotypes most enriched in the training set of islands are followed to detect yet more islands. Component panGenome.pl produces a big-data visualization of the chromosomally-ordered "pan-genome", that includes every gene of every reference genome (x-axis, pan-genome order; y-axis, reference genomes; color-coding, gene presence/absence etc.), islands appearing as dark patches.« less

To initiate an order for this software, request consultation services, or receive further information, fill out the request form below. You may also reach us by email at: .

OSTI staff will begin to process an order for scientific and technical software once the payment and signed site license agreement are received. If the forms are not in order, OSTI will contact you. No further action will be taken until all required information and/or payment is received. Orders are usually processed within three to five business days.

Software Request

(required)
(required)
(required)
(required)
(required)
(required)
(required)
(required)