Benchmarks are essential tools for the optimizer's development. Using them, we can check for what kind of problems a given optimizer is effective or not. Since the objective of the Evolutionary Computation field is to support the tools to solve hard, real-world problems, the benchmarks that resemble their features seem particularly valuable. Therefore, we propose a hop-based analysis of the optimization process. We apply this analysis to the NP-hard, large-scale real-world problem. Its results indicate the existence of some of the features of the well-known Leading Ones problem. To model these features well, we propose the Leading Blocks Problem (LBP), which is more general than Leading Ones and some of the benchmarks inspired by this problem. LBP allows for the assembly of new types of hard optimization problems that are not handled well by the considered state-of-the-art genetic algorithm (GA). Finally, the experiments reveal what kind of mechanisms must be proposed to improve GAs' effectiveness while solving LBP and the considered real-world problem.

翻译：基准测试是优化器开发的重要工具。借助基准测试，我们可以检验给定优化器对何种类型的问题有效或无效。由于进化计算领域的目标是提供解决复杂现实世界问题的工具，因此能够模拟现实问题特征的基准测试显得尤为宝贵。为此，我们提出了一种基于跳跃的优化过程分析方法。我们将该分析方法应用于NP难的大规模现实世界问题，其结果表明该问题存在著名Leading Ones问题的某些特征。为精准建模这些特征，我们提出了Leading Blocks Problem（LBP）——该问题比Leading Ones及受其启发的部分基准测试更具普适性。LBP能够构建新型复杂优化问题，而当前考虑的先进遗传算法（GA）难以有效处理此类问题。最终，实验揭示了在求解LBP及所考察的现实世界问题时，必须提出何种改进机制以提升遗传算法的效能。