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中不可或缺的工具。借助它们，我们可以检验给定优化器在何种问题上有效或无效。由于进化计算领域的宗旨是支持解决复杂现实世界问题的工具开发，因此那些能够模拟这些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变过程的优化分析方法。我们将该方法应用于一个NP难题的大规模现实世界问题。分析结果表明，该问题展现了经典Leading Ones问题的一些特征。为对这些特征进行有效建模，我们提出了Leading Blocks问题(LBP)，该问题比Leading Ones问题及其衍生基准测试更具一般性。LBP能够组合生成新型复杂优化问题，而当前最先进的遗传算法(GA)在处理这些问题时表现不佳。最终，实验揭示了在求解LBP及该现实世界问题时，需要提出何种机制方能提升GA的有效性。