等高线辅助进化计算：从已评估解的等高线估计峰值
Contour-Assisted Evolutionary Computation: Estimating Peaks From the Contour Lines of Evaluated Solutions
AI 导读
一篇 arXiv 论文提出把进化算法已评估的候选解视为地形测量点并绘制等高线，以估计峰的位置、高度和形状。在 CA-EA 中加入等高线步骤、局部搜索和峰值记忆后，二维基准上遗传算法所需评估次数可减少 12 到 112 倍；面向昂贵问题的 CA-SAEA 在 CEC 2015 套件 10 与 30 维上平均排名第二，仅次于 DYCORS，领先于 lq-CMA-ES 和 MVMO。
正文 · 原文
Abstract:Evolutionary algorithms (EAs) evaluate many candidate solutions but use them mainly to rank individuals. This paper treats the evaluated solutions as the spot heights of a topographic survey and draws the contour lines they imply. The contour map decomposes the evaluated region into peaks: in two dimensions a contour tree with persistence separates significant peaks from interpolation ripples, and in more dimensions hill climbing on the interpolated surface locates the peaks and ray-cast level sets describe them. A memory of the explored peaks directs the search to new ones. The data-supported contours of each peak are extrapolated to zero size by an area law, exact for quadratic peaks and second-order accurate for smooth ones, that estimates a peak's location, height and shape before it is sampled. A contour-assisted EA (CA-EA) inserts contour steps with local search and peak memory into a genetic algorithm (GA), differential evolution or particle swarm optimization. A contour-assisted surrogate-assisted EA (CA-SAEA), for expensive problems, spends each evaluation on surrogate-screened offspring or on sampling around a peak, entered at the maximum of the interpolated surface. On 2-D benchmarks, CA-EA reduces the evaluations a plain GA needs by factors of 12 to 112; controls attribute its gain over classical memetic search to restarting local search on distinct peaks. On the CEC 2015 expensive suite in 10 and 30 dimensions, CA-SAEA has the second-best average rank, behind DYCORS and ahead of lq-CMA-ES and the competition winner MVMO, although in 30 dimensions MVMO wins significantly on 9 functions and loses on 6; there the map beats sampling around the best point on 5 of 15 functions and loses on 3. Entering the peaks at their extrapolated locations brought no gain at these budgets, although, with enough samples, it improves the estimated heights on most two-dimensional functions.
| Comments: | 15 pages; supplementary material (10 pages) is provided as an ancillary file |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Optimization and Control (math.OC) |
| Cite as: | arXiv:2611.15641 [cs.NE] |
| (or arXiv:2611.15641v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15641 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jun Zhang [view email]
[v1]
Wed, 11 Nov 2026 15:53:55 UTC (2,990 KB)
来源：arXiv cs.AI · arxiv.org