Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems
提出一种基于最近更好关系的适应度景观分析方法，通过四个数值测量和三维可视化有效刻画连续优化问题的中性、崎岖性、模态和吸引盆特征，帮助理解算法在高维问题中的搜索行为。
Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this article, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3-D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: 1) neutrality; 2) ruggedness; 3) modality; and 4) Basin of Attraction, respectively, and common features of the fitness landscape can be maintained in 3-D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3-D NBN visualization.