发表机构
OsloMet – Oslo Metropolitan University; Simula Research Laboratory; University of Oslo; Kristiania University of Applied Sciences（奥斯陆城市大学; 西穆拉研究实验室; 奥斯陆大学; 克里斯蒂尼亚应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
通过进化模拟研究有限可耗竭景观中的搜索策略，发现进化搜索更符合间歇性动态而非Lévy行走，为资源受限探索提供启示。
AI 中文摘要
在有限、可耗竭的景观中，搜索策略如何进化仍是觅食理论中的一个问题。我们通过进化模拟研究该问题，其中智能体在包含均匀分布或Lévy尘埃形式的不可再生资源的二维环形格点上觅食。每个智能体携带一个可遗传的基因组，编码步长、速度和转向角，选择作用于结合能量收益、移动成本和覆盖效率的适应度函数。通过允许移动性状进化而不强加预设的幂律步长分布，我们检验进化轨迹是否更符合间歇性搜索或Lévy行走动态。结果表明，在本文考虑的有限耗竭驱动景观中，进化搜索更符合间歇性动态而非严格无标度Lévy运动。我们通过将二阶和四阶位移矩拟合到间歇性搜索和Lévy行走模型来表征动态。虽然类似Lévy的随机行走能很好地拟合进化轨迹（大多数测试条件下平均调整$R^2$ > 0.9），但间歇性搜索在所有测试资源分布下均实现更优拟合（平均调整$R^2$ > 0.99）。这一偏好适用于所有测试的网格尺寸和资源密度。在503 x 503网格、标称资源密度$\ ho$ = 0.15下，每个环境进行五次独立进化运行，均匀环境和五个Lévy尘埃环境均重现此偏好。进化迅速将移动基因组重塑为短位移，同时保留稀疏的长距离迁移尾部，这与局部开发伴随偶尔转移一致。该框架为研究资源限制下搜索规则的出现提供了受控环境，并可能为自主系统中的资源受限探索提供参考。
英文摘要
How search strategies evolve in finite, depletable landscapes remains a question in foraging theory. We study this problem with an evolutionary simulation in which agents forage on a two-dimensional toroidal lattice containing non-renewable resources distributed uniformly or as Lévy dust. Each agent carries a heritable genome encoding step lengths, velocities, and turning angles, and selection acts on a fitness function combining energetic gain, movement cost, and coverage efficiency. By allowing movement traits to evolve without imposing a prescribed power-law step-length distribution, we test whether evolved trajectories are better described by intermittent-search or Lévy-walk dynamics. Our results indicate that evolved search is more consistent with intermittent dynamics than with strict scale-free Lévy motion in the finite depletion-driven landscapes considered here. We characterize the dynamics by fitting second- and fourth-order displacement moments to intermittent-search and Lévy-walk models. While a Lévy-like random walk fits the evolutionary trajectories well (mean adjusted $R^2$ > 0.9 in most tested conditions), intermittent search achieves a closer fit (mean adjusted $R^2$ > 0.99) for all tested resource distributions. This preference holds across the tested grid sizes and resource densities. Five independent evolutionary runs per environment on a 503 x 503 grid at nominal resource density $ρ$ = 0.15 reproduce this preference for the uniform environment and five Lévy-dust environments. Evolution rapidly reshapes the movement genome toward short displacements while retaining a sparse tail of longer relocations, consistent with local exploitation punctuated by occasional transfer. The framework provides a controlled setting for studying how search rules emerge under resource limitation and may inform resource-constrained exploration in autonomous systems.
CommentsAccepted at NeurIPS 2026. Project webpage: https://evo-foraging.github.io/
