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Related Concept Videos

Heuristics01:21

Heuristics

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...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
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...

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Related Experiment Video

Updated: May 26, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Hyper-heuristics with low level parameter adaptation.

Zhilei Ren1, He Jiang, Jifeng Xuan

  • 1School of Mathematical Sciences, Dalian University of Technology, Dalian, China. ren@mail.dlut.edu.cn

Evolutionary Computation
|December 17, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive hyper-heuristic approach to automatically manage low-level parameters, enhancing problem-solving efficiency. The integration of heuristic space reduction improves exploration for better results in complex optimization problems.

Related Experiment Videos

Last Updated: May 26, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computer Science

Background:

  • Hyper-heuristics offer general search methodologies by automating algorithm design.
  • Low-level heuristics (LLHs) within hyper-heuristics are often parameterized, challenging domain independence.
  • Adaptive parameter management is crucial for enhancing hyper-heuristic performance and generality.

Purpose of the Study:

  • To introduce a novel hyper-heuristic framework with automatic low-level parameter (LLP) adaptation (AD-HH).
  • To integrate a heuristic space reduction (SAR) mechanism to address search space expansion.
  • To evaluate the effectiveness of AD-HH with SAR on the p-median problem.

Main Methods:

  • Developed an adaptive hyper-heuristic (AD-HH) model for automatic LLP maintenance.
  • Implemented a heuristic space reduction (SAR) mechanism to optimize the search process.
  • Tested the AD-HH and SAR integrated framework on diverse p-median problem instances.

Main Results:

  • AD-HH successfully adapted LLPs for two hyper-heuristic models, demonstrating feasibility.
  • The integration of AD-HH with SAR improved heuristic space exploration efficiency.
  • The proposed algorithms achieved competitive results across heterogeneous benchmark instances.

Conclusions:

  • Automatic LLP adaptation within hyper-heuristics is feasible and beneficial.
  • The SAR mechanism effectively mitigates search space expansion issues.
  • The combined approach offers a powerful strategy for solving complex optimization problems like the p-median problem.