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Related Concept Videos

Crossover Experiments01:16

Crossover Experiments

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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.
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Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
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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.
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Decision Making: P-value Method01:09

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack

John H Drake1, Ender Özcan2, Edmund K Burke3

  • 1School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK drakejohnh@gmail.com.

Evolutionary Computation
|January 31, 2015
PubMed
Summary
This summary is machine-generated.

This study explores using crossover in selection hyper-heuristics for complex problems. Managing crossover at the domain level, using problem-specific information, significantly outperforms managing it at the hyper-heuristic level.

Keywords:
Combinatorial optimisationhyper-heuristicslocal searchmetaheuristic.multidimensional knapsack problem

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computer Science

Background:

  • Hyper-heuristics are advanced methods for tackling complex problems by managing heuristics.
  • Selection hyper-heuristics choose from a pool of low-level heuristics to guide search.
  • The integration and optimization of crossover operators within hyper-heuristics remain underexplored.

Purpose of the Study:

  • To investigate novel frameworks for utilizing crossover low-level heuristics in selection hyper-heuristics.
  • To compare two conceptual levels of crossover control: hyper-heuristic level and problem domain level.
  • To assess the effectiveness of these frameworks on an NP-hard optimization problem.

Main Methods:

  • Proposed frameworks maintain lists of candidate solutions for crossover operations.
  • Crossover control was implemented at the hyper-heuristic level, requiring no problem-specific data.
  • Crossover control was also implemented at the problem domain level, leveraging problem-specific insights.
  • Performance was evaluated using selection hyper-heuristics across three benchmark libraries for the multidimensional 0-1 knapsack problem.

Main Results:

  • Frameworks managing crossover at the problem domain level demonstrated superior performance.
  • Domain-level management, utilizing problem-specific information, yielded better solution quality.
  • Hyper-heuristic level management of crossover was less effective in this context.

Conclusions:

  • Managing crossover at the problem domain level is a more effective strategy for selection hyper-heuristics.
  • Leveraging problem-specific knowledge enhances the utility of crossover operators.
  • This research provides valuable insights for optimizing hyper-heuristic approaches for NP-hard problems.