Analysis of the performance of algorithm configurators for search heuristics with global mutation operators
Analysis of the performance of algorithm configurators for search heuristics with global mutation operators
复制标题
使用全局变异算子进行搜索启发式算法配置器的性能分析
DOI：
10.1145/3377930.3390218
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Dirk Sudholt
中科院分区：
文献类型：
--
作者：
George T. Hall;P. S. Oliveto;Dirk Sudholt
最近，它已被证明，一个简单的算法配置称为ParamRLS可以有效地识别随机局部搜索优化两个标准的基准问题类所使用的最佳邻域大小。在本文中，我们分析了性能的算法配置器调整更复杂的全局变异算子中使用的标准进化算法，翻转每个n位独立的概率为χ/n和最佳值的χ已被确定。我们比较性能的配置时，最好的发现在截止时间内的健身值k被用来比较配置对实际优化时间为两个标准的基准问题类，岭和LeadingOnes。我们严格证明，所有的算法配置器，使用优化时间作为性能指标，需要截止时间，至少是一样大的预期优化时间，以确定最佳配置。如果使用适应性度量，情况就大不相同了。为了证明这一点，我们证明了简单的ParamRLS-F配置器可以确定最佳的突变率，即使使用的截止时间是大大小于预期的优化时间的最佳参数值为两个问题类。
Recently it has been proved that a simple algorithm configurator called ParamRLS can efficiently identify the optimal neighbourhood size to be used by stochastic local search to optimise two standard benchmark problem classes. In this paper we analyse the performance of algorithm configurators for tuning the more sophisticated global mutation operator used in standard evolutionary algorithms, which flips each of the n bits independently with probability χ/n and the best value for χ has to be identified. We compare the performance of configurators when the best-found fitness values within the cutoff time k are used to compare configurations against the actual optimisation time for two standard benchmark problem classes, Ridge and LeadingOnes. We rigorously prove that all algorithm configurators that use optimisation time as performance metric require cutoff times that are at least as large as the expected optimisation time to identify the optimal configuration. Matters are considerably different if the fitness metric is used. To show this we prove that the simple ParamRLS-F configurator can identify the optimal mutation rates even when using cutoff times that are considerably smaller than the expected optimisation time of the best parameter value for both problem classes.
影响因子：
3.6
作者：
Burke, Edmund K.;Gendreau, Michel;Qu, Rong
通讯作者：
Qu, Rong