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Selection Heuristics on Semantic Genetic Programming for Classification Problems.

Claudia N Sánchez1,2, Mario Graff3

  • 1Universidad Panamericana. Facultad de Ingeniería. Aguascalientes, 20296, México.

Evolutionary Computation
|October 25, 2021
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Summary
This summary is machine-generated.

This study introduces novel semantic similarity heuristics for parent selection in Genetic Programming. These methods improve classifier performance, outperforming random and existing selection schemes.

Keywords:
Genetic programmingclassificationfunctions' propertiesparent selectionsemantics

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Genetic Programming (GP) utilizes individual semantics to guide learning.
  • Previous work explored genetic operators and parent selection using semantics.
  • Parent selection based on semantic similarity is a key area for GP enhancement.

Purpose of the Study:

  • To propose and evaluate three novel heuristics for parent selection in Genetic Programming based on semantic similarity.
  • To investigate the use of function properties for guiding the GP learning process.
  • To assess the effectiveness of these heuristics in enhancing specific classifiers like Naive Bayes and Nearest Centroid.

Main Methods:

  • Developed three semantic similarity heuristics: cosine similarity, Pearson's correlation, and agreement.
  • Applied heuristics for parent selection in Genetic Programming, focusing on offspring generation.
  • Compared performance against random selection, state-of-the-art schemes, and 18 classifiers across 30 diverse classification problems.

Main Results:

  • The combination of agreement-based parent selection and random replacement yielded statistically superior results.
  • The proposed heuristics demonstrated competitive performance against state-of-the-art classifiers.
  • This marks the first use of function properties to guide GP learning through semantic similarity.

Conclusions:

  • Semantic similarity-based parent selection offers a promising avenue for improving Genetic Programming.
  • The agreement heuristic, combined with random replacement, provides a robust and effective selection strategy.
  • The open-source release facilitates further research and application of these techniques.