abstract

A genetic algorithm for linear ordering problem using an approximate fitness evaluation

Authors:

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 1461 - 1462

https://doi.org/10.1145/2598394.2602282

Published: 12 July 2014 Publication History

Get Access

Abstract

Genetic algorithms are widely used to solve combinatorial optimization problems, but they often take a long time. Usually, generating and evaluating a large number of different solutions spend most of the running time. We propose a genetic algorithm for the linear ordering problem which uses an approximate fitness evaluation. We use a part of the edges to compute the fitness function value, and the number of the edges for this is gradually increased during the evolutionary process. We present experimental results on the benchmark library LOLIB. The approximation scheme reduced the running time without loss of solution quality in general.

References

[1]

M. Grötschel, M. Jünger, and G. Reinelt. A cutting plane algorithm for the linear ordering problem. Operations Research, 32(6):1195--1220, 1984.

Digital Library

Google Scholar

[2]

R. Martí, G. Reinelt, and A. Duarte. A benchmark library and a comparison of heuristic methods for the linear ordering problem. Comput. Optim. Appl., 51(3):1297--1317, Apr. 2012.

Digital Library

Google Scholar

[3]

T. Schiavinotto and T. Stützle. The linear ordering problem: Instances, search space analysis and algorithms. Journal of Mathematical Modelling and Algorithms, 3(4):367--402, 2005.

Crossref

Google Scholar

Cited By

View all

Cao JHuang HLiu F(2021)Android Unit Test Case Generation Based on the Strategy of Multi-Dimensional Coverage2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)10.1109/CCIS53392.2021.9754637(114-121)Online publication date: 7-Nov-2021
https://doi.org/10.1109/CCIS53392.2021.9754637
Pham DHuynh TNguyen Q(2015)The new crossover operators and a novel combination of crossover operators for solving Linear Ordering Problem2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI.2015.7407081(150-157)Online publication date: Nov-2015
https://doi.org/10.1109/TAAI.2015.7407081

Index Terms

A genetic algorithm for linear ordering problem using an approximate fitness evaluation
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
  2. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

Recommendations

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an ...
Designing a hybrid genetic algorithm for the linear ordering problem
GECCO'03: Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI

The Linear Ordering Problem(LOP), which is a well-known NP-hard problem, has numerous applications in various fields. Using this problem as an example, we illustrate a general procedure of designing a hybrid genetic algorithm, which includes the ...
Solving the Graph Planarization Problem Using an Improved Genetic Algorithm

An improved genetic algorithm for solving the graph planarization problem is presented. The improved genetic algorithm which is designed to embed a graph on a plane, performs crossover and mutation conditionally instead of probability. The improved ...

Comments

Information & Contributors

Information

Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2014

Check for updates

Author Tags

Qualifiers

Abstract

Funding Sources

Ministry of Science, ICT and Future Planning

Conference

GECCO '14

Sponsor:

SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

Acceptance Rates

GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
122
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 08 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Cao JHuang HLiu F(2021)Android Unit Test Case Generation Based on the Strategy of Multi-Dimensional Coverage2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)10.1109/CCIS53392.2021.9754637(114-121)Online publication date: 7-Nov-2021
https://doi.org/10.1109/CCIS53392.2021.9754637
Pham DHuynh TNguyen Q(2015)The new crossover operators and a novel combination of crossover operators for solving Linear Ordering Problem2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI)10.1109/TAAI.2015.7407081(150-157)Online publication date: Nov-2015
https://doi.org/10.1109/TAAI.2015.7407081

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

Designing a hybrid genetic algorithm for the linear ordering problem

Solving the Graph Planarization Problem Using an Improved Genetic Algorithm

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations