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

Evolutionary algorithms and a fractal inverse problem

D J Nettleton1, R Garigliano

  • 1Department of Computer Science, University of Durham Science Site, UK.

Bio Systems
|January 1, 1994
PubMed
Summary

Evolutionary algorithms, inspired by natural evolution, offer robust search methods. Evolutionary programming proved more effective than genetic algorithms for a fractal inverse problem due to its focus on phenotypic adaptation.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same journal

A hydration-scaffold framework for phase-sensitive coupling and collective organization in biomolecular water.

Bio Systems·2026
Same journal

The noncomputability of immune reaction complexity: Algorithmic information gaps under effective constraints.

Bio Systems·2026
Same journal

Computational design and experimental validation of fast oligonucleotide-sensing allosteric ribozymes with predefined oligonucleotide binding sites.

Bio Systems·2026
Same journal

Way to operationalization of the life process definition 'from purposeful information' resulting from Abel's proposal; mechanisms of evolutionary progress.

Bio Systems·2026
Same journal

Cold-Selective Topological Bias and the Emergence of the First Membranes.

Bio Systems·2026
Same journal

Quantifying quantum-like structure in rough set lattices: Numerical indices for complex and intelligent systems.

Bio Systems·2026

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • Evolutionary algorithms (EAs) are robust search methods inspired by natural evolution.
  • EAs have broad applications across various problem domains.
  • Two prominent EAs are genetic algorithms (GAs) and evolutionary programming (EP).

Purpose of the Study:

  • To compare the efficacy of genetic algorithms and evolutionary programming.
  • To investigate their application in shape representation, specifically a fractal inverse problem.
  • To understand the reasons behind performance disparities between GAs and EP.

Main Methods:

  • Discussed two types of evolutionary algorithms: genetic algorithms and evolutionary programming.
  • Applied these algorithms to a shape representation problem involving a fractal inverse problem.
  • Analyzed the underlying evolutionary principles driving genotypic transformations (GAs) versus phenotypic adaptation (EP).

Main Results:

  • Evolutionary programming demonstrated success in solving the fractal inverse problem.
  • Genetic algorithms showed less success in addressing the same problem.
  • Disparities in performance were attributed to differing emphases on genotypic versus phenotypic adaptation.

Conclusions:

  • Evolutionary programming's focus on phenotypic adaptation is advantageous for certain problems like fractal inverse problems.
  • Genetic algorithms, while powerful, may be less suited for problems where direct phenotypic adaptation is key.
  • The study highlights the importance of algorithm design choices in evolutionary computation for specific applications.

Related Experiment Videos