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

Implicit representation in genetic algorithms using redundancy.

A M Raich1, J Ghaboussi

  • 1Department of Civil Engineering, University of Illinois, Urbana 61801-2397, USA. awilkiso@ews.uiuc.edu

Evolutionary Computation
|February 18, 1999
PubMed
Summary
This summary is machine-generated.

A novel implicit redundant representation (IRR) enhances genetic algorithms (GAs) by dynamically managing parameters. This biologically inspired approach improves population diversity and search space flexibility for complex problems.

Related Experiment Videos

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

Effect of cornea material stiffness on measured intraocular pressure.

Journal of biomechanics·2008
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

Area of Science:

  • Computational Intelligence
  • Evolutionary Computation
  • Bio-inspired Computing

Background:

  • Traditional genetic algorithms (GAs) often struggle with unstructured problem domains due to rigid representations.
  • Existing GA methods require users to predefine parameter locations and quantities, limiting flexibility.
  • The need for adaptive and flexible representations in evolutionary computation is critical for complex problem-solving.

Purpose of the Study:

  • To introduce a new Implicit Redundant Representation (IRR) for genetic algorithms.
  • To demonstrate the superior performance of IRR compared to simple and structured GAs.
  • To enable GAs to handle unstructured problem domains without explicit constraints.

Main Methods:

  • Developed an over-specified string representation where sections can be inactive during evaluation.
  • Fitness functions implicitly determine essential parameters and their locations during GA operations.
  • Implemented and experimentally compared the IRR-based GA against standard and structured GA approaches.

Main Results:

  • The IRR-based GA significantly outperformed simple and structured GAs in experimental tests.
  • IRR reduces disruption of fit individuals by focusing mutations and crossovers on redundant data.
  • Redundant material is converted into essential information, facilitating the discovery of novel solutions.

Conclusions:

  • The Implicit Redundant Representation (IRR) offers a more biologically parallel and flexible approach to genetic algorithms.
  • IRR dynamically adjusts the search space and maintains population diversity, crucial for evolutionary processes.
  • This representation is highly effective for unstructured problems lacking explicit constraints, enhancing GA applicability.