Problem
Research questions and friction points this paper is trying to address.
Evolutionary Transfer Optimization
Task-Parameterized Applications
Evaluation Scaling
Towards Efficient Evaluation of Evolutionary Transfer Optimization: Case Studies on Task-Parameterized Applications
🤖 AI Summary
研究通过将任务参数化应用中的特定计算重新表述为适合并行执行的形式，解决了进化迁移优化(ETO)在扩展到更大任务集合时的评估效率问题。
📝 Abstract
As evolutionary transfer optimization (ETO) scales to larger collections of related tasks, problem evaluation can become a major source of runtime growth. This work studies problem-side evaluation scaling in task-parameterized applications and reformulates application-specific serial computations into forms suitable for parallel execution. We organize evaluation scaling into two levels: the number of evaluated tasks and the workload within each task. In multi-task optimization, matrix-recursive kinematic-arm evaluation is reformulated using an accumulation-matrix representation of cumulative link directions. In sequential transfer optimization, pointwise B-spline trajectory evaluation is reformulated using a blending-matrix representation for trajectory and collision computations. Both reformulations maintain close numerical agreement with their reference evaluations and substantially reduce runtime, yielding $256.72\times$ and $93.91\times$ end-to-end speedups, respectively. These results demonstrate problem-side reformulation as a practical route toward scalable ETO. Both application implementations and experimental scripts are released as open source to support reproducibility and reuse.
Innovation
Methods, ideas, or system contributions that make the work stand out.
evolutionary transfer optimization
parallel execution
accumulation-matrix representation
blending-matrix representation
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