A Parallel Genetic Algorithm for the Set Partitioning Problem

One of 1,403 items in the series: Argonne National Laboratory Reports available on this site.
Thumbnail image of item number 1 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.
Thumbnail image of item number 2 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.
Thumbnail image of item number 3 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.
Thumbnail image of item number 4 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.
Showing 1-4 of 90 pages in this report.

PDF Version Also Available for Download.

Description

In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning … continued below

Physical Description

xii, 78 p. : tables

Creation Information

Levine, David May 1994.

Context

This report is part of the collection entitled: Technical Report Archive and Image Library and was provided by the UNT Libraries Government Documents Department to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 149 times. More information about this report can be viewed below.

Who

People and organizations associated with either the creation of this report or its content.

Author

Originators

Publisher

Audiences

We've identified this report as a primary source within our collections. Researchers, educators, and students may find this report useful in their work.

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

About | Browse this Partner

Contact Us

Corrections Questions

What

Descriptive information to help identify this report. Follow the links below to find similar items on the Digital Library.

Titles

  • Main Title: A Parallel Genetic Algorithm for the Set Partitioning Problem
  • Added Title: ANL (Series)
  • Added Title: Argonne National Laboratory Report ANL-94/23
  • Series Title: Argonne National Laboratory Reports

Description

In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. The authors found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulation found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation they found was the difficulty solving problems with many constraints.

Physical Description

xii, 78 p. : tables

Notes

Digitized from microfiche (1).

Some parts of this report may be illegible, depending on the quality of the microfiche.

Language

Item Type

Identifier

Unique identifying numbers for this report in the Digital Library or other systems.

  • OCLC: 880595993
  • SuDoc Number: Y 3.AT 7:22/ANL-94/23
  • Report No.: ANL-94/23
  • Grant Number: W-31-109-Eng-38
  • Archival Resource Key: ark:/67531/metadc283162

Collections

This report is part of the following collection of related materials.

Technical Report Archive and Image Library

The Technical Report Archive & Image Library (TRAIL) identifies, acquires, catalogs, digitizes and provides unrestricted access to U.S. government agency technical reports. The mission of TRAIL is to ensure preservation, discoverability, and persistent open access to government technical publications regardless of form or format.

About | Browse this Collection

What responsibilities do I have when using this report?

Digital Files

When

Dates and time periods associated with this report.

Creation Date

  • May 1994

Added to The UNT Digital Library

  • Aug. 4, 2015, 8:33 a.m.

Usage Statistics

When was this report last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 149
More Statistics

Interact With This Report

Here are some suggestions for what to do next.

Start Reading

Thumbnail image of item number 1 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.
Thumbnail image of item number 2 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.
Thumbnail image of item number 3 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.
Thumbnail image of item number 4 in: 'A Parallel Genetic Algorithm for the Set Partitioning Problem'.

PDF Version Also Available for Download.

Citations, Rights, Re-Use

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

Links for Robots

Helpful links in machine-readable formats.

Archival Resource Key (ARK)

International Image Interoperability Framework (IIIF)

Metadata Formats

Images

URLs

Stats

Levine, David. A Parallel Genetic Algorithm for the Set Partitioning Problem, report, May 1994; Argonne, Illinois. (https://digital.library.unt.edu/ark:/67531/metadc283162/: accessed June 8, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.

Back to Top of Screen