NSGA-II: Implementation and Performance Metrics Extraction for CPU and GPU

Florina Roxana Padurariu, C. Marinescu
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引用次数: 1

Abstract

Multi-objective Optimization Evolutionary Algorithms are widely employed for solving different real-world optimization problems. Usually their runs involve a considerable amount of time because of the need to evaluate many functions. This particularity makes them good candidates of parallelization. In this work we investigate the benefits of the GPU implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) versus its CPU implementation in terms of the execution time.
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NSGA-II: CPU和GPU的实现和性能指标提取
多目标优化进化算法被广泛应用于解决现实世界中的各种优化问题。通常,由于需要评估许多函数,它们的运行涉及相当多的时间。这种特殊性使它们成为并行化的良好候选对象。在这项工作中,我们研究了GPU实现非主导排序遗传算法II (NSGA-II)与CPU实现在执行时间方面的优势。
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