Evolutionary algorithms are well suited to neural architecture search and other combinatorial design problems, but their scalability is often limited by the high cost of fitness evaluation. This paper studies evolutionary multi-objective optimisation in a regime where fitness evaluations are effectively free, enabled by a training-free proxy for neural network expressivity. We propose SWAP-Lite, a Pareto-guided evolutionary algorithm that maintains an explicit archive of non-dominated solutions over representational capacity and deployment cost, yielding an anytime optimiser that exposes budget-feasible solutions throughout the search. Using a MobileNet-style architecture space as a case study, we instantiate the fitness function with a sample-wise activation pattern proxy and perform large-scale evolutionary searches with up to 105 architecture evaluations. Experiments on CIFAR-10 and ImageNet show that SWAP-Lite discovers compact architectures that are competitive with state-of-the-art training-based and zero-shot baselines, while reducing search cost by one to four orders of magnitude. Analysis of the evolutionary dynamics demonstrates that explicit bi-objective optimisation produces higher-quality constrained Pareto fronts and superior anytime hypervolume compared with random search, greedy local search, and single-objective evolutionary baselines.

Zero-shot proxies, also known as training-free metrics, are widely adopted to reduce the computational overhead in neural network evaluation for scenarios such as Neural Architecture Search (NAS), as they do not require any training. Existing zero-shot metrics have several limitations, including weak correlation with the true performance and poor generalisation across different networks or downstream tasks. For example, most of these metrics apply only to either convolutional neural networks (CNNs) or Transformers, but not both. To address these limitations, we propose Sample-Wise Activation Patterns (SWAP), and its derivative, SWAP-Score, a novel and highly effective zero-shot metric. SWAP-Score is broadly applicable across both architecture families and task domains, demonstrating strong predictive performance in the majority of tasks. This metric measures the expressivity of neural networks over a mini-batch of samples, showing a high correlation with the neural networks' ground-truth performance. For both CNNs and Transformers, the SWAP-Score outperforms existing zero-shot metrics across computer vision and natural language processing tasks. For instance, Spearman's correlation coefficient between the SWAP-Score and CIFAR-10 validation accuracy for DARTS CNNs is 0.93, and 0.71 for FlexiBERT Transformers on GLUE tasks. Moreover, SWAP-Score is label-independent, hence can be applied at the pre-training stage of language models to estimate their performance for downstream tasks. When applied to NAS, SWAP-empowered NAS, SWAP-NAS can achieve competitive performance using only approximately 6 and 9 minutes of GPU time, on CIFAR-10 and ImageNet respectively.

Training-free metrics (a.k.a. zero-cost proxies) are widely used to avoid resource-intensive neural network training, especially in Neural Architecture Search (NAS). Recent studies show that existing training-free metrics have several limitations, such as limited correlation and poor generalisation across different search spaces and tasks. Hence, we propose Sample-Wise Activation Patterns and its derivative, SWAP-Score, a novel high-performance training-free metric. It measures the expressivity of networks over a batch of input samples. The SWAP-Score is strongly correlated with ground-truth performance across various search spaces and tasks, outperforming 15 existing training-free metrics on NAS-Bench-101/201/301 and TransNAS-Bench-101. The SWAP-Score can be further enhanced by regularisation, which leads to even higher correlations in cell-based search space and enables model size control during the search. For example, Spearman’s rank correlation coefficient between regularised SWAP-Score and CIFAR-100 validation accuracies on NAS-Bench-201 networks is 0.90, significantly higher than 0.80 from the second-best metric, NWOT. When integrated with an evolutionary algorithm for NAS, our SWAP-NAS achieves competitive performance on CIFAR-10 and ImageNet in approximately 6 minutes and 9 minutes of GPU time respectively.

Evolutionary neural architecture search (ENAS) has emerged as a promising approach to finding high-performance neural architectures. However, widespread application has been limited by the expensive computational costs due to the nature of evolutionary algorithms. In this study, we aim to significantly reduce the computational costs of ENAS by involving a training-free performance metric. Specifically, the network performance can be estimated by the training-free metric with only a single forward pass. However, training-free metrics have their own challenges, in particular, an insufficient correlation with ground-truth performance. We adopt a Graph Convolutional Network (GCN) based contrastive predictor which can leverage the low cost of the training-free performance metric yet improve the correlation between the estimated performance and the true performance of the candidate architectures. Combining a training-free metric - the number of linear regions with the GCN-based contrastive predictor and an active learning scheme, we propose Fast-ENAS which can achieve superior search efficiency and performance on the benchmark NAS-Bench-201 and DARTS search spaces. Furthermore, with a single GPU searching on the DARTS space, Fast-ENAS requires only 0.02 (29 minutes) and 0.026 (37 minutes) GPU days to achieve test error rates of 2.50% and 24.30% on CIFAR-10 and ImageNet respectively.

Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space. Prediction of the performance of a network can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, predictor-assisted evolutionary NAS (PRE-NAS) which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous. This circumvents bias and leads to more accurate predictions. Extensive experiments on the NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, a competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet, respectively.

Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search. Prediction of the networks' performance can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, Predictor-assisted E-NAS (PRE-NAS), which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous, which circumvents bias and leads to more accurate predictions. Extensive experiments on NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet respectively.

The purpose of convolutional neural networks is usually image classification but there are increasing studies attempting to reverse this common purpose in order to generate images. One of the most promising research directions is style transfer. This involves rendering the overall texture of an image into an artistic style. There are two common approaches in this field, which are feature representation based methods and generative adversarial network(GAN) based methods. In this paper, we focus on GAN based methods. We observed that most variants of GAN usually need paired data in order to generate the desired result, the training costs are very heavy and the quality of the result is not guaranteed. We propose an improved architecture for generative adversarial models for multi-style rendering. A new loss function configuration enables learning from unpaired data and generation of stylized images with specific artistic styles from normal photographs. A weighted combination of loss functions can control the trade-off between style and content of a stylized image.