Low Rank Factorizations are Indirect Encodings for Deep Neuroevolution

• URL: http://arxiv.org/abs/2504.03037v1
• Date: Thu, 03 Apr 2025 21:31:48 GMT
• Title: Low Rank Factorizations are Indirect Encodings for Deep Neuroevolution
• Authors: Jack Garbus, Jordan Pollack,
• Abstract summary: We introduce low-rank, factorized neuroevolution: an indirect encoding through which we can search a small space of low-rank factors.<n>We evaluate our method on a language modeling task using transformers, as well as continuous and discrete vision-based reinforcement learning tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neuroevolution is a highly scalable alternative to reinforcement learning due to its unique ability to encode network updates in a small number of bytes. Recent insights from traditional deep learning indicate high-dimensional models possess intrinsic, low-rank structure. In this work, we introduce low-rank, factorized neuroevolution: an indirect encoding through which we can search a small space of low-rank factors that enforce underlying structure across a network's weights. We compare our approach with non-factorized networks of similar and smaller size to understand how much performance can be attributed to the smaller search space. We evaluate our method on a language modeling task using transformers, as well as continuous and discrete vision-based reinforcement learning tasks. Our study shows that low-rank, factorized neuroevolution outperforms or is competitive with non-factorized neuroevolution, performing notably well on language modeling. Our results also suggest deleterious factorized mutations have a stronger negative impact on performance than deleterious non-factorized mutations, which significantly reduces the runtime on environments with early termination for bad performers. More broadly, these results show how we can use insights from backpropgation-based methods to enhance neuroevolution

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