FIFO and LRU caching significantly reduce fitness evaluation time in GPSR, with FIFO performing surprisingly well despite its simplicity?

FIFO and LRU caching significantly reduce fitness evaluation time in GPSR, with FIFO performing surprisingly well despite its simplicity

Complex caching mechanisms need a minimum cache size before yielding computational time reductions, unlike lightweight strategies?

Complex caching mechanisms need a minimum cache size before yielding computational time reductions, unlike lightweight strategies

Empirical analysis of key-value usage with infinite cache provides concrete guidance for optimal cache sizing on synthetic and real-world datasets

Research & Papers

arXiv cs.NE August 03, 2026

⚡Lightweight caching strategies like FIFO beat complex methods on fitness evaluations, surprising researchers.

Deep Dive

Key Points

FIFO and LRU caching significantly reduce fitness evaluation time in GPSR, with FIFO performing surprisingly well despite its simplicity
Complex caching mechanisms need a minimum cache size before yielding computational time reductions, unlike lightweight strategies
Empirical analysis of key-value usage with infinite cache provides concrete guidance for optimal cache sizing on synthetic and real-world datasets

Simpler cache choices can dramatically speed up symbolic regression workloads, reducing compute costs for ML and optimization pipelines.