The Beagle framework, through GPU-based Genetic Programming, enables population dynamics previously unattainable (within practical time frames) by CPU-constrained Genetic Programming systems. This work explores how GPU-enabled population sizes impact the success of training for symbolic regression problems. Specifically, when using constant population sizes, we see benefits of using very narrow and deep searches (as narrow as 1000 individuals) for some problems, while other problems benefit from very broad and shallow searches (as broad as 10 million individuals). We also explore stepped population sizes that start with large populations and drop to small populations to balance the breadth and depth of search.

翻译：Beagle框架通过基于GPU的基因编程，实现了此前受限于CPU的基因编程系统在实用时间范围内无法企及的种群动态。本研究探索了GPU支持的种群规模如何影响符号回归问题的训练成功率。具体而言，当采用恒定种群规模时，我们发现某些问题受益于极窄和深度搜索（窄至1000个体），而另一些问题则受益于极宽和浅层搜索（宽至1000万个个体）。我们还研究了从大种群逐步缩减至小种群的阶梯式种群规模策略，以平衡搜索的广度与深度。