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Open Access

ARTICLE

Adaptive Meta-Loss Networks: Learning Task-Agnostic Loss Functions via Evolutionary Optimization

Mirna Yunita1, Xiabi Liu1,*, Zhaoyang Hai1, Rachmat Muwardi2
1 School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China
2 Department of Electrical Engineering, Universitas Mercu Buana, Jakarta, Indonesia
* Corresponding Author: Xiabi Liu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.075073

Received 24 October 2025; Accepted 30 December 2025; Published online 14 February 2026

Abstract

Designing appropriate loss functions is critical to the success of supervised learning models. However, most conventional losses are fixed and manually designed, making them suboptimal for diverse and dynamic learning scenarios. In this work, we propose an Adaptive Meta-Loss Network (Adaptive-MLN) that learns to generate task-agnostic loss functions tailored to evolving classification problems. Unlike traditional methods that rely on static objectives, Adaptive-MLN treats the loss function itself as a trainable component, parameterized by a shallow neural network. To enable flexible, gradient-free optimization, we introduce a hybrid evolutionary approach that combines Genetic Algorithms (GA) for global exploration and Evolution Strategies (ES) for local refinement. This co-evolutionary process dynamically adjusts the loss landscape, improving model generalization without relying on analytic gradients or handcrafted heuristics. Experimental evaluations on synthetic tasks and the CIFAR-10 and MNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy, convergence, and adaptability.

Keywords

Meta-learning; adaptive loss function; task-agnostic optimization; evolutionary strategy; genetic algorithm; classification

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