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

Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by
Same author

Seismogeodetic P-wave Amplitude: No Evidence for Strong Determinism.

Geophysical research letters·2020
Same author

Geodetic Observations of Weak Determinism in Rupture Evolution of Large Earthquakes.

Journal of geophysical research. Solid earth·2019
Same author

Use of genetic algorithms with multivariate regression for determination of gelatine in historic papers based on FT-IR and NIR spectral data.

Talanta·2010
Same author

Mechanism of DNA substrate recognition by the mammalian DNA repair enzyme, Polynucleotide Kinase.

Nucleic acids research·2009
Same author

The nature and frequency of neovascular age-related macular degeneration.

European journal of ophthalmology·2007
Same author

Hemoglobin degradation.

Current topics in microbiology and immunology·2005
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 Videos

Using previous models to bias structural learning in the hierarchical BOA.

M W Hauschild1, M Pelikan, K Sastry

  • 1Missouri Estimation of Distribution Algorithms Laboratory (MEDAL), Department of Computer Science, University of Missouri at St. Louis, St. Louis, Missouri 63121, USA. mwh308@umsl.edu

Evolutionary Computation
|November 16, 2011
PubMed
Summary
This summary is machine-generated.

Estimation of distribution algorithms (EDAs) can learn from past runs. This study introduces methods to bias future runs using learned models, significantly speeding up solutions for similar problems.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computational Optimization
  • Machine Learning

Background:

  • Estimation of Distribution Algorithms (EDAs) are stochastic optimization techniques that build probabilistic models to find optimal solutions.
  • EDAs generate a sequence of probabilistic models containing valuable problem-specific information.
  • This inherent information within EDAs has been largely underutilized for improving performance on similar problems.

Purpose of the Study:

  • To leverage probabilistic models generated by EDAs to accelerate the solving of similar problems.
  • To introduce novel approaches for biasing model building in hierarchical Bayesian optimization algorithms (hBOA).
  • To enhance the efficiency of solving multiple related optimization tasks.

Main Methods:

  • Developed two distinct approaches to bias model building in hBOA.
  • Utilized knowledge automatically learned from previous hBOA runs on analogous problems.
  • Focused on incorporating problem-specific insights derived from probabilistic models.

Main Results:

  • Demonstrated substantial speedups in solving problems using the proposed methods.
  • Validated the effectiveness of using learned probabilistic models for future optimization tasks.
  • Showcased significant performance improvements in hBOA through knowledge transfer.

Conclusions:

  • The proposed methods offer a viable strategy for accelerating optimization processes.
  • Biasing model building with learned knowledge from EDAs is effective for similar problems.
  • These techniques hold promise for applications involving numerous problems with shared structures.