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

Autonomic Nervous System01:22

Autonomic Nervous System

13.0K
The autonomic nervous system (ANS) is a critical component of the peripheral nervous system, primarily responsible for regulating involuntary bodily functions and maintaining homeostasis. It functions in tandem with the central nervous system (CNS) to seamlessly coordinate various physiological processes without the need for conscious control.
The ANS comprises two main divisions: the sympathetic and parasympathetic divisions. These divisions function antagonistically to maintain a dynamic...
13.0K
Autonomic Nervous System: Overview01:26

Autonomic Nervous System: Overview

7.6K
The human nervous system is divided into two main parts: the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS is composed of the brain and spinal cord, while the PNS contains nerve cells, clusters of nerve cells, and the sensory receptors that are outside the CNS. The PNS has two types of nerve cells: sensory (afferent) and motor (efferent). Sensory cells send signals to the CNS from receptors, and motor cells carry signals from the CNS to organs, muscles, and...
7.6K
Disorders of the Autonomic Nervous System01:18

Disorders of the Autonomic Nervous System

1.6K
The autonomic nervous system (ANS) is an intricate network of nerves that controls functions such as the regulation of heart rate, digestion, and blood pressure regulation. When this system malfunctions, it can lead to various disorders that affect multiple bodily functions. One common feature of many autonomic disorders is the involvement of smooth blood vessels, which play a crucial role in regulating blood flow throughout the body.
Raynaud's disease, also known as Raynaud's...
1.6K
Drugs Acting on Autonomic Ganglia: Stimulants01:23

Drugs Acting on Autonomic Ganglia: Stimulants

2.1K

Ganglionic stimulants activate NM nicotinic receptors in autonomic ganglia, falling into two categories: nicotine mimetics [e.g., lobeline, dimethylpiperazine, tetramethylammonium] and muscarinic receptor agonists [e.g., muscarine, methacholine]. The first category's action is rapid and blocked by nicotinic receptor antagonists, while the second category's action is delayed and blocked by atropine-like agents. Nicotine, an alkaloid, affects the heart rate by stimulating...
2.1K
Drugs Acting on Autonomic Ganglia: Blockers01:28

Drugs Acting on Autonomic Ganglia: Blockers

1.7K
Ganglionic blockers inhibit autonomic activity by blocking nicotinic receptors in the autonomic ganglia, suppressing impulse transmission. These blockers lack selectivity between sympathetic and parasympathetic ganglia and are ineffective as neuromuscular junction antagonists. They can be categorized into two groups:
1.7K
Simple Pendulum01:10

Simple Pendulum

8.1K
A simple pendulum consists of a small diameter ball suspended from a string, which has negligible mass but is strong enough to not stretch. In our daily life, pendulums have many uses, such as in clocks, on a swing set, and on a sinker on a fishing line. 
The period of a simple pendulum depends on two factors: its length and the acceleration due to gravity. The period is completely independent of any other factors, such as mass or maximum displacement. For small displacements, a pendulum is...
8.1K

You might also read

Related Articles

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

Sort by
Same author

On Stochastic Operators, Fitness Landscapes, and Optimization Heuristics Performances.

Evolutionary computation·2025
Same author

Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes.

Evolutionary computation·2016
Same author

Improvements on bicriteria pairwise sequence alignment: algorithms and applications.

Bioinformatics (Oxford, England)·2013
Same author

On local search for bi-objective knapsack problems.

Evolutionary computation·2012
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
  1. Home
  3. Adaptive Sampled Walk: A Simple And Efficient Autonomous Local Search.
  1. Home
  3. Adaptive Sampled Walk: A Simple And Efficient Autonomous Local Search.

Related Experiment Video

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.9K

Adaptive Sampled Walk: A Simple and Efficient Autonomous Local Search.

Matthieu Basseur1, Arnaud Liefooghe2, Sara Tari3

  • 1Univ. Littoral Côte d'Opale, UR 4491, LISIC, F-62100 Calais, France matthieu.basseur@univ-littoral.fr.

Evolutionary Computation
|February 10, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed parameter-free local search algorithms that automatically adapt search behavior. These autonomous methods achieve robust results on diverse optimization problems without extensive tuning.

Keywords:
Combinatorial optimizationevolutionary computationfitness landscapeslocal searchparameter control

More Related Videos

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication
03:53

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication

Published on: November 17, 2023

1.6K
Walk with Me Hybrid Virtual/In-Person Walking for Older Adults with Neurodegenerative Disease
07:21

Walk with Me Hybrid Virtual/In-Person Walking for Older Adults with Neurodegenerative Disease

Published on: June 16, 2023

1.6K

Related Experiment Videos

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.9K
Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication
03:53

Author Spotlight: Exploring Behavioral Pathways Through Cross-Species Insights in Foraging and Communication

Published on: November 17, 2023

1.6K
Walk with Me Hybrid Virtual/In-Person Walking for Older Adults with Neurodegenerative Disease
07:21

Walk with Me Hybrid Virtual/In-Person Walking for Older Adults with Neurodegenerative Disease

Published on: June 16, 2023

1.6K

Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computer Science

Background:

  • Traditional local search methods often require extensive parameter tuning for optimal performance.
  • Developing autonomous search algorithms that adapt to problem characteristics is a key challenge.

Purpose of the Study:

  • To introduce and explore automated and adaptive partial neighborhood local search algorithms.
  • To design local search methods that operate with minimal prerequisites and no parameter tuning.

Main Methods:

  • Extended sampled walk and ID walk algorithms.
  • Incorporated distance-based calculations over a sliding window to dynamically determine neighborhood evaluation size.
  • Developed parameter-free local search techniques.

Main Results:

  • Empirically evaluated parameter-free methods on four benchmark combinatorial optimization problem classes.
  • Compared performance against fixed-parameter versions.
  • Demonstrated robust and competitive results across diverse problems with varying solution representations, neighborhood structures, and fitness landscapes.

Conclusions:

  • Parameter-free local search approaches are viable and effective.
  • Autonomous local search methods offer a generic and robust alternative to parameter-tuned algorithms.
  • The proposed methods validate the potential of generic autonomous local search.