GPU加速的并行基因库灰盒优化混合算法
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting
摘要：
在允许部分评估的灰盒优化（GBO）设置中，个体的适应度可以在其变量的子集被修改后有效地更新。这使得更有效的进化优化与基因库最优混合进化算法（GOMEA），由于其关键优势：基因库最优混合（GOM）。对于每个解决方案，GOM执行许多（小）变量集的变化。为了进一步提高效率，可以利用并行计算。对于EA，通常，这包括群体并行化。然而，除非人口规模很大，否则这带来的收益有限。对于大型GBO问题，并行GOM为基础的变化拥有更大的加速潜力，无论人口规模。然而，由于变量之间的依赖性，这种潜力无法直接利用。我们展示了如何使用图着色来分组变量集，这些变量集可以并行地进行变化而不违反依赖关系。我们测试性能的CUDA实现并行GOM的图形处理单元（GPU）的最大切割问题，一个众所周知的问题，依赖结构可以控制。我们发现，对于连通性有限的足够大的图，找到高质量的解决方案可以快100倍，展示了我们方法的巨大潜力。
英文摘要：
In a Gray-Box Optimization (GBO) setting that allows for partial evaluations, the fitness of an individual can be updated efficiently after a subset of its variables has been modified. This enables more efficient evolutionary optimization with the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) due to its key strength: Gene-pool Optimal Mixing (GOM). For each solution, GOM performs variation for many (small) sets of variables. To improve efficiency even further, parallel computing can be leveraged. For EAs, typically, this comprises population-wise parallelization. However, unless population sizes are large, this offers limited gains. For large GBO problems, parallelizing GOM-based variation holds greater speed-up potential, regardless of population size. However, this potential cannot be directly exploited because of dependencies between variables. We show how graph coloring can be used to group sets of variables that can undergo variation in parallel without violating dependencies. We test the performance of a CUDA implementation of parallel GOM on a Graphics Processing Unit (GPU) for the Max-Cut problem, a well-known problem for which the dependency structure can be controlled. We find that, for sufficiently large graphs with limited connectivity, finding high-quality solutions can be achieved up to 100 times faster, showcasing the great potential of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
