Evolutionary foraging in grids: Intermittent search dynamics emerge in finite, depletable landscapes

2026-09-30

AI Summary

This study investigates whether intermittent search or Lévy walks are more advantageous in finite, depletable environments. To address this question, we construct a resource-consuming environment on a two-dimensional grid and employ genetic algorithms to drive the free evolution of movement strategies, thereby eliminating assumptions regarding predefined power-law distributions. The dynamical characteristics of the resulting search trajectories are analyzed by integrating Lévy dust modeling with second- and fourth-order displacement moment fitting. Our findings reveal that, under resource-constrained conditions, intermittent search consistently outperforms strict Lévy motion, achieving model fitting coefficients exceeding R² > 0.99. This work provides a theoretical foundation for designing exploration strategies employed by autonomous systems operating within environments characterized by finite resources.

Abstract

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.

Problem

• foraging theory
• evolutionary search
• intermittent search
• Lévy walk
• depletable landscapes

Innovation

• Evolutionary foraging
• Intermittent search
• Lévy walk
• Finite depletable landscapes
• Movement genome