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

A derived Markov process for modeling reaction networks.

John H Holland1

  • 1Department of EECS and Department of Psychology, The University of Michigan, Ann Arbor, MI 48109, USA. holland@umich.edu

Evolutionary Computation
|November 25, 2003
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

A proposal for a Decade of the Mind initiative.

Science (New York, N.Y.)·2007
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 models reaction networks using stochastic urn models and Markov processes. These methods allow for efficient estimation of key process properties, like the time to reach specific outcomes.

Area of Science:

  • Multidisciplinary science
  • Computational modeling
  • Stochastic processes

Background:

  • Reaction networks involve reactants recombining to form new substances.
  • Characterizing reactants by "reactive regions" is key to modeling.
  • Classic stochastic urn models can represent these networks.

Purpose of the Study:

  • To model complex reaction networks using established mathematical frameworks.
  • To enable efficient estimation of dynamic properties within these networks.
  • To provide a computational approach for analyzing reaction trajectories.

Main Methods:

  • Modeling reaction networks as stochastic urn processes.
  • Utilizing Markov processes defined by matrices.
  • Applying standard matrix operations and Monte Carlo simulations.

Related Experiment Videos

Main Results:

  • Demonstrated the applicability of stochastic urn models to reaction networks.
  • Showcased the efficiency of matrix operations for realistic problem sizes.
  • Enabled Monte Carlo estimation of critical process metrics.

Conclusions:

  • Stochastic urn models provide a robust framework for analyzing reaction networks.
  • Computational methods allow for efficient prediction of reaction dynamics.
  • This approach facilitates understanding of complex system behavior.