Abstract

Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw vectorial data during training, not only enables Vec-GP to select appropriate aggregation functions itself, but also allows Vec-GP to extract segments from vectors prior to aggregation (like windows for time series data). This is a critical factor in many machine learning applications, as vectors can be very long and only small segments may be relevant. However, allowing aggregation over segments within GP models makes the training more complicated. We explore the use of common evolutionary algorithms to help GP identify appropriate segments, which we analyze using a simplified problem that focuses on optimizing aggregation segments on fixed data. Since the studied algorithms are to be used in GP for local optimization (e.g. as mutation operator), we evaluate not only the quality of the solutions, but also take into account the convergence speed and anytime performance. Among the evaluated algorithms, CMA-ES, PSO and ALPS show the most promising results, which would be prime candidates for evaluation within GP.

OriginalspracheEnglisch
TitelGECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten439-442
Seitenumfang4
ISBN (elektronisch)9798400701207
DOIs
PublikationsstatusVeröffentlicht - 15 Juli 2023
Veranstaltung2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion - Lisbon, Portugal
Dauer: 15 Juli 202319 Juli 2023

Publikationsreihe

NameGECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion

Konferenz

Konferenz2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion
Land/GebietPortugal
OrtLisbon
Zeitraum15.07.202319.07.2023

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