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 Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Limit Laws I01:25

Limit Laws I

Limit laws provide essential tools for analyzing how functions behave as their input approaches a specific value. These laws are particularly useful when dealing with combinations of functions, provided the individual limits exist. The Sum and Difference Laws state that the limit of the sum or difference of two functions equals the sum or difference of their respective limits:The Product Law asserts that the limit of the product of two functions equals the product of their individual limits:A...
Free Energy and Equilibrium00:55

Free Energy and Equilibrium

The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔG is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
The reaction quotient, Q, is a convenient measure of the status of an...
Free Energy and Equilibrium02:56

Free Energy and Equilibrium

The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔGrxn is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
Recall that Q is the numerical value of the mass action expression...

You might also read

Related Articles

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

Sort by
Same author

Tabula rasa agents display emergent in-group behavior.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Perceptual interventions ameliorate statistical discrimination in learning agents.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Emotions and courtship help bonded pairs cooperate, but emotional agents are vulnerable to deceit.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

The Puzzle of Evaluating Moral Cognition in Artificial Agents.

Cognitive science·2023
Same author

What is the simplest model that can account for high-fidelity imitation?

The Behavioral and brain sciences·2022
Same author

Learning agents that acquire representations of social groups.

The Behavioral and brain sciences·2022
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

Related Experiment Video

Updated: May 23, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

No free lunch and benchmarks.

Edgar A Duéñez-Guzmán1, Michael D Vose

  • 1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. duenez@oeb.harvard.edu

Evolutionary Computation
|March 29, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces new theoretical tools for black box search algorithms, focusing on the no free lunch theorem under restricted conditions. It provides insights into algorithm performance and matching for specific benchmarks.

More Related Videos

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Related Experiment Videos

Last Updated: May 23, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Area of Science:

  • Computer Science
  • Theoretical Computer Science
  • Optimization

Background:

  • Black box optimization algorithms are widely used but their performance guarantees are often limited.
  • The No Free Lunch (NFL) theorem states that no single algorithm is universally superior across all possible problems.
  • Previous NFL research often assumed unrestricted problem and algorithm classes.

Purpose of the Study:

  • To extend the theoretical understanding of the No Free Lunch (NFL) theorem.
  • To analyze algorithm performance under restricted function benchmarks, algorithm collections, or step limitations.
  • To explore minimax distinctions and performance matching from a geometric viewpoint.

Main Methods:

  • Development of new theoretical tools for analyzing black box search algorithms.
  • Application of geometric perspectives to understand minimax distinctions.
  • Investigation of performance matching under constrained conditions.

Main Results:

  • New theoretical results extending the NFL theorem to restricted settings.
  • Demonstration of how function restrictions, algorithm collections, or step limits affect performance.
  • Presentation of basic results on performance matching.

Conclusions:

  • Algorithm performance is highly dependent on the specific problem class and algorithm restrictions.
  • The developed theoretical tools offer a more nuanced understanding of optimization algorithm limitations.
  • Future research can build upon these findings for more tailored algorithm design.