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High-Performance Parallel Implementation of Genetic Algorithm on FPGA

机译：遗传算法在FPGA上的高性能并行实现

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Genetic algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a full-parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system's processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposed in this paper is able to work with more variable from some adjustments on hardware architecture. The results showed that the GA full-parallel implementation achieved throughput about 16 millions of generations per second and speedups between 17 and 170,000 associated with several works proposed in the literature.
机译：遗传算法（GA）用于解决搜索和优化问题，在这些问题中，可以使用具有概率和非确定性转换的迭代过程找到最佳解决方案。但是，根据问题的性质，由于遗传算法的计算复杂性，在顺序机器中找到解决方案所需的时间可能会很长。这项工作提出了在现场可编程门阵列（FPGA）上遗传算法的完全并行实现。优化系统的处理时间是该项目的主要目标。分析与各种人口规模的处理时间和面积占用（在FPGA上）相关的结果。还评估了有关用于优化两个变量功能的GA响应精度的研究，以用于硬件实现。但是，本文提出的高性能实现可以通过对硬件体系结构进行一些调整来使用更多变量。结果表明，GA完全并行的实现实现了每秒约1600万代的吞吐量，并且与文献中提出的多项工作相关联的加速比在17到170,000之间。

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