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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
107
Heuristics01:21

Heuristics

156
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
156
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

155
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
155
Multiple Allele Traits01:49

Multiple Allele Traits

35.1K
The Concept of Multiple Allelism
35.1K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

307
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
307
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

166
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
166

You might also read

Related Articles

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

Sort by
Same author

Towards Richer Assisted Living Environments.

SN computer science·2021
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: Sep 22, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K

When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization.

Fernando G Lobo1, Mosab Bazargani2

  • 1DEEI-FCT & CENSE - Center for Environmental and Sustainability Research & CHANGE - Global Change and Sustainability Institute, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal flobo@ualg.pt.

Evolutionary Computation
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

Multistart next ascent hillclimbing outperforms evolutionary algorithms on multimodal optimization problems. This is because evolutionary algorithms struggle with landscapes lacking structure, suggesting brute-force approaches may be superior.

Keywords:
Multimodal optimizationevolutionary algorithmshill-climbingmultimodal problem generatorniching

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K

Related Experiment Videos

Last Updated: Sep 22, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Multimodal optimization problems present challenges for search algorithms due to multiple local optima.
  • The multimodal problem generator creates complex bitstring landscapes, generalizing classical OneMax and TwoMax functions.
  • Understanding algorithm performance on these landscapes is crucial for advancing optimization techniques.

Purpose of the Study:

  • To compare the performance of multistart next ascent hillclimbing with evolutionary algorithms on a multimodal problem generator.
  • To analyze the average-case runtime of hillclimbing on specific instances of these problems.
  • To determine the effectiveness of diversity preservation techniques in evolutionary algorithms for multimodal optimization.

Main Methods:

  • Implementation and empirical testing of multistart next ascent hillclimbing.
  • Application of evolutionary algorithms with niching and mating restriction techniques.
  • Theoretical average-case runtime analysis for hillclimbing on uniformly distributed, equal-height problem instances.

Main Results:

  • Multistart next ascent hillclimbing demonstrated superior performance compared to evolutionary algorithms.
  • Conventional diversity preservation methods in evolutionary algorithms were insufficient for competitiveness.
  • The lack of structure in the problem's local optima space hinders information exploitation by algorithms.

Conclusions:

  • Evolutionary algorithms with standard diversity techniques are not always effective for multimodal optimization.
  • Hillclimbing can be a competitive strategy, especially on fitness landscapes with unstructured local optima.
  • Brute-force strategies may be optimal for discovering all solutions in landscapes lacking exploitable structure.