Locality is a crucial property for efficiently optimising black-box problems with randomized search heuristics. However, in practical applications, it is not likely to always find such a genotype encoding of candidate solutions that this property is upheld with respect to the Hamming distance. At the same time, it may be possible to use domain-specific knowledge to define a metric with locality property. We propose two mutation operators to solve such optimization problems more efficiently using the metric. The first operator assumes prior knowledge about the distance, the second operator uses the distance as a black box. Those operators apply an estimation of distribution algorithm to find the best mutant according to the defined in the paper function, which employs the given distance. For pseudo-boolean and integer optimization problems, we experimentally show that both mutation operators speed up the search on most of the functions when applied in considered evolutionary algorithms and random local search. Moreover, those operators can be applied in any randomized search heuristic which uses perturbations. However, our mutation operators increase wall-clock time and so are helpful in practice when distance is (much) cheaper to compute than the real objective function.

翻译：局部性是基于随机搜索启发式算法高效优化黑箱问题的一个关键属性。然而，在实际应用中，很难总能找到候选解的基因型编码，使得该属性在汉明距离下得以保持。同时，可以利用领域特定知识来定义具有局部性属性的度量。我们提出两种变异算子，以利用该度量更高效地求解此类优化问题。第一种算子假设已知距离的先验知识，第二种算子则将距离作为黑箱使用。这些算子应用分布估计算法，根据本文所定义的函数（该函数利用了给定的距离）来寻找最优变异体。针对伪布尔和整数优化问题，我们通过实验表明，在考虑采用的进化算法和随机局部搜索中应用这两种变异算子时，它们在大多数函数上加快了搜索速度。此外，这些算子可应用于任何使用扰动的随机搜索启发式算法中。然而，我们的变异算子增加了挂钟时间，因此在实际中，当距离计算（远）比实际目标函数评估更廉价时，它们才具有实用价值。