Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Evolving combinatorial problem instances that are difficult to solve.

Jano I van Hemert1

  • 1National e-Science Centre, University of Edinburgh, United Kingdom. http://www.vanhemert.co.uk/

Evolutionary Computation
|November 18, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Performance database: capturing data for optimizing distributed streaming workflows.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2011
Same author

Validation and mismatch repair of workflows through typed data streams.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2011
Same author

A user-friendly web portal for T-Coffee on supercomputers.

BMC bioinformatics·2011
Same author

Automatically identifying and annotating mouse embryo gene expression patterns.

Bioinformatics (Oxford, England)·2011
Same author

An open source toolkit for medical imaging de-identification.

European radiology·2010
Same author

Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles.

BMC genomics·2010
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles

This study uses evolutionary computation to create challenging combinatorial optimization problems. These evolved instances stress-test algorithms in constraint satisfaction, Boolean satisfiability, and the traveling salesman problem, revealing algorithmic weaknesses.

Area of Science:

  • Computational intelligence
  • Combinatorial optimization

Background:

  • Combinatorial optimization problems are crucial in computer science and operations research.
  • Existing benchmarks may not adequately stress-test algorithms for solving complex instances.
  • Evolutionary computation offers a novel approach to generate challenging problem instances.

Purpose of the Study:

  • To demonstrate the use of evolutionary computation for generating difficult combinatorial problem instances.
  • To stress-test algorithms by applying them to these novel, challenging instances.
  • To analyze the structural properties of evolved instances to understand algorithm weaknesses.

Main Methods:

  • Applying evolutionary computation to generate problem instances.
  • Utilizing the generated instances to stress-test algorithms.

Related Experiment Videos

  • Analyzing the structural properties of the evolved problem instances.
  • Focusing on domains including binary constraint satisfaction, Boolean satisfiability, and the traveling salesman problem.

    • Main Results:

    • Successfully generated problem instances that are more difficult than those in popular benchmarks.
    • Evolved instances effectively stress-tested algorithms across multiple combinatorial optimization domains.
    • Identified specific structural properties contributing to the difficulty of evolved instances.
    • Exposed weaknesses in existing algorithms when confronted with these challenging instances.

    Conclusions:

    • Evolutionary computation is an effective method for creating difficult combinatorial optimization problems.
    • The generated instances provide valuable benchmarks for rigorously evaluating algorithm performance.
    • Understanding the structural properties of problem instances is key to improving algorithm design and robustness.