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Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction

Published: 15 November 2023 Publication History

Abstract

In recent years, genetic programming-based evolutionary feature construction has shown great potential in various applications. However, a critical challenge in applying this technique is the need to select an appropriate selection operator with great care. To tackle this issue, this paper introduces a novel approach that leverages the Thompson sampling technique to automatically choose the optimal selection operator based on semantic information of genetic programming models gathered during the evolutionary process. The experimental results on a standard symbolic regression benchmark containing 37 datasets show that the proposed adaptive operator selection algorithm outperforms expert-designed operators, demonstrating the effectiveness of the adaptive operator selection algorithm.

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Published In

cover image Guide Proceedings
PRICAI 2023: Trends in Artificial Intelligence: 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023, Jakarta, Indonesia, November 15–19, 2023, Proceedings, Part II
Nov 2023
514 pages
ISBN:978-981-99-7021-6
DOI:10.1007/978-981-99-7022-3

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 November 2023

Author Tags

  1. Genetic Programming
  2. Evolutionary Feature Construction
  3. Adaptive Operator Selection

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