NEVO-GSPT: Population-Based Neural Network Evolution Using Inflate and Deflate Operators

• URL: http://arxiv.org/abs/2601.08657v1
• Date: Tue, 13 Jan 2026 15:35:16 GMT
• Title: NEVO-GSPT: Population-Based Neural Network Evolution Using Inflate and Deflate Operators
• Authors: Davide Farinati, Frederico J. J. B. Santos, Leonardo Vanneschi, Mauro Castelli,
• Abstract summary: glsngspt is a novel Neuroevolution algorithm based on two key innovations.<n>glsngspt consistently evolves compact neural networks that achieve performance comparable to or better than established methods.
• Score: 3.513869875017041
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evolving neural network architectures is a computationally demanding process. Traditional methods often require an extensive search through large architectural spaces and offer limited understanding of how structural modifications influence model behavior. This paper introduces \gls{ngspt}, a novel Neuroevolution algorithm based on two key innovations. First, we adapt geometric semantic operators~(GSOs) from genetic programming to neural network evolution, ensuring that architectural changes produce predictable effects on network semantics within a unimodal error surface. Second, we introduce a novel operator (DGSM) that enables controlled reduction of network size, while maintaining the semantic properties of~GSOs. Unlike traditional approaches, \gls{ngspt}'s efficient evaluation mechanism, which only requires computing the semantics of newly added components, allows for efficient population-based training, resulting in a comprehensive exploration of the search space at a fraction of the computational cost. Experimental results on four regression benchmarks show that \gls{ngspt} consistently evolves compact neural networks that achieve performance comparable to or better than established methods in the literature, such as standard neural networks, SLIM-GSGP, TensorNEAT, and SLM.

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