NK景观的一次改进与最佳改进局部最优网络
First-improvement vs. Best-improvement Local Optima Networks of NK Landscapes
摘要：
本文扩展了最近提出的组合景观模型：局部最优网络（LON），将第一次改进（贪婪上升）爬山算法，而不是最好的改进（最陡上升）之一，定义和提取流域的景观最优的吸引力。一组NK景观的最佳和第一次改进网络模型的统计分析比较，提出和讨论。我们的研究结果表明，这两个模型之间的结构差异，无论是网络的连接，和性质的盆地的吸引力。深入讨论了这些差异对基于第一和最佳改进局部搜索的搜索策略行为的影响。
英文摘要：
This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedy-ascent) hill-climbing algorithm, instead of a best-improvement (steepest-ascent) one, for the definition and extraction of the basins of attraction of the landscape optima. A statistical analysis comparing best and first improvement network models for a set of NK landscapes, is presented and discussed. Our results suggest structural differences between the two models with respect to both the network connectivity, and the nature of the basins of attraction. The impact of these differences in the behavior of search heuristics based on first and best improvement local search is thoroughly discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
