Benchmark Instances

该数据集旨在通过向量遗传编程优化特征提取的片段，生成的基准实例代表了不同挑战和难度级别。这些实例是通过生成随机控制值，并将其用作各种分布中的参数来创建的。此外，基准实例包含了不同的特征，如变化的斜率和噪声特性，这对于评估优化策略的表现至关重要。该数据集的特征规模根据所使用的参数而有所不同，其任务在于遗传编程中针对片段聚合的参数优化。

This dataset is designed to optimize the segments of feature extraction via Vector Genetic Programming. The generated benchmark instances represent different challenges and difficulty levels. These instances are created by generating random control values and using them as parameters in various distributions. Additionally, the benchmark instances include diverse features such as varying slopes and noise characteristics, which are critical for evaluating the performance of optimization strategies. The feature scale of this dataset varies depending on the parameters used, and its task focuses on parameter optimization for segment aggregation in genetic programming.