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In this work, we introduce DyESP, a novel approach that unites dynamic exploration with space pruning to expedite the combined search of hyperparameters and architecture, enhancing the efficiency and accuracy of hyperparameter-architecture search (HAS). Central to DyESP are two innovative components: a meta-scheduler that customizes the search strategy for varying spaces and a pruner designed to minimize the hyperparameter space by discarding suboptimal configurations. The meta-scheduler leverages historical data to dynamically refine the search direction, targeting the most promising areas while minimizing unnecessary exploration. Meanwhile, the pruner employs a surrogate model, specifically a fine-tuned multilayer perceptron (MLP), to predict and eliminate inferior configurations based on static metrics, thereby streamlining the search and conserving computational resources. The results from the pruner, which identifies and removes underperforming configurations, are fed into the meta-scheduler. This process updates the historical dataset used by the meta-scheduler, enabling it to adjust the exploration degree and refine the sampling strategy for subsequent iterations. This integration ensures the meta-scheduler is continually updated with relevant data, allowing for more accurate and timely adjustments to the exploration strategy.Experiments on various benchmarks show that DyESP outperforms existing methods in terms of both speed and stability on almost all benchmarks.