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On Search Trajectory Networks for Graph Genetic Programming

Published: 01 August 2024 Publication History

Abstract

Cartesian Genetic Programming (CGP) allows for the optimization of interpretable function representations. However, comprehending the vast and combinatorially complex search space inherent to CGP remains challenging, particularly because multiple genotypes may correspond to identical functions. This paper studies the application of Search Trajectory Networks (STNs) to understand the search dynamics of CGP, specifically for symbolic regression tasks. Using STNs, we analyze the behavior of evolutionary search processes and uncover distinct phenomena, such as the presence of "portal" minima---critical junctures that facilitate sudden, beneficial shifts in the search trajectory, akin to findings in linear genetic programming. Our findings illustrate that while genetic interpretations are complex and often ambiguous, a functional analysis using STNs offers clear and actionable insights into the CGP search.

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cover image ACM Conferences
GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2024
2187 pages
ISBN:9798400704956
DOI:10.1145/3638530
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Published: 01 August 2024

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  1. genetic programming
  2. evolutionary computation
  3. symbolic regression

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