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Upsampling01:22

Upsampling

363
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
363

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Problem-Solving Benefits of Down-Sampled Lexicase Selection.

Thomas Helmuth1, Lee Spector2,3,4

  • 1Hamilton College thelmuth@hamilton.edu.

Artificial Life
|September 2, 2021
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Summary
This summary is machine-generated.

Down-sampled lexicase selection enhances genetic programming by evaluating more individuals per generation. This approach improves problem-solving power by allowing a broader evolutionary search within a fixed computational budget.

Keywords:
Genetic programmingdown-sampled lexicase selectionlexicase selectionparent selectionprogram synthesis

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Parent selection in genetic programming typically uses aggregate performance on all training data.
  • Lexicase selection improves problem-solving by using performance on random training case sequences.
  • Down-sampling further enhances lexicase selection by using random subsets of training cases each generation.

Purpose of the Study:

  • To extensively benchmark down-sampled lexicase selection in genetic programming.
  • To investigate the underlying reasons for the benefits of down-sampling.

Main Methods:

  • Systematic evaluation of down-sampled lexicase selection.
  • Benchmarking against existing methods.
  • Testing hypotheses regarding the benefits of down-sampling.

Main Results:

  • Down-sampled lexicase selection demonstrates significant benefits under extensive scrutiny.
  • Evidence was found against hypotheses related to increased generations, changing environments, or reduced overfitting.
  • The primary benefit is attributed to examining more individuals within the same computational budget.

Conclusions:

  • Down-sampled lexicase selection is a powerful technique for enhancing genetic programming.
  • The main advantage lies in increased population exploration due to evaluating more individuals, not in other proposed mechanisms.
  • Future research can build upon these findings for more effective evolutionary algorithms.