Low Rank Factorizations are Indirect Encodings for Evolved Neural Networks

Conference proceeding

Low Rank Factorizations are Indirect Encodings for Evolved Neural Networks

Jack Garbus and Jordan Pollack

GECCO 2025 Companion - Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion, pp.719-722

08/11/2025

DOI: https://doi.org/10.1145/3712255.3726564

Abstract

Deep Neuroevolution

Genetic Algorithms

Indirect Encoding

Transformers

Metrics

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Details

Title: Low Rank Factorizations are Indirect Encodings for Evolved Neural Networks
Creators: Jack Garbus - Brandeis University
Jordan Pollack - Brandeis University
Publication Details: GECCO 2025 Companion - Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion, pp.719-722
Identifiers: 9924526927601921
Academic Unit: Michtom School of Computer Science
Language: English
Resource Type: Conference proceeding