Selecting Simulation Algorithm Portfolios by Genetic Algorithms
Selecting Simulation Algorithm Portfolios by Genetic Algorithms
复制标题
通过遗传算法选择仿真算法组合
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
A. Uhrmacher
中科院分区：
文献类型：
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作者：
Roland Ewald;Rene Schulz;A. Uhrmacher
算法组合是一组捆绑在一起以提高整体性能的算法。虽然到目前为止主要应用于计算难题，但我们研究了模拟算法的组合选择，并重点关注它们在自适应模拟复制中的应用。由于投资组合选择问题本身很难解决，因此我们引入遗传算法来从大量模拟算法中选择最有前途的投资组合。该机制的有效性是通过来自实际性能研究和专用测试环境的数据来评估的。
An algorithm portfolio is a set of algorithms that are bundled together for increased overall performance. While being mostly applied to computationally hard problems so far, we investigate portfolio selection for simulation algorithms and focus on their application to adaptive simulation replication. Since the portfolio selection problem is itself hard to solve, we introduce a genetic algorithm to select the most promising portfolios from large sets of simulation algorithms. The effectiveness of this mechanism is evaluated by data from both a realistic performance study and a dedicated test environment.