论文标题
随机搜索启发式方法的参数化复杂性分析
Parameterized Complexity Analysis of Randomized Search Heuristics
论文作者
论文摘要
本章汇编了许多结果，这些结果将参数化算法理论应用于随机搜索启发式方法（例如进化算法）的运行时间分析。参数化方法比经典复杂性理论的传统方法表达了算法解决组合问题的运行时间。我们概述了用于解决NP-HARD组合优化问题的随机搜索启发式集合的主要结果和证明技术，例如在图中找到最小顶点覆盖物，在图中找到最大的叶子生成树，以及旅行销售人员问题。
This chapter compiles a number of results that apply the theory of parameterized algorithmics to the running-time analysis of randomized search heuristics such as evolutionary algorithms. The parameterized approach articulates the running time of algorithms solving combinatorial problems in finer detail than traditional approaches from classical complexity theory. We outline the main results and proof techniques for a collection of randomized search heuristics tasked to solve NP-hard combinatorial optimization problems such as finding a minimum vertex cover in a graph, finding a maximum leaf spanning tree in a graph, and the traveling salesperson problem.
