Track genomics, neuroscience and molecular biology.
Search papers, labs, and topics across Lattice.
This work investigates the evolution of search strategies in finite, depletable 2D landscapes by simulating agents with heritable movement traits (step lengths, velocities, turning angles) under selection pressures for energetic gain, movement cost, and coverage efficiency. The study tests whether evolved trajectories align with intermittent-search or L\'evy-walk dynamics by fitting displacement moments without pre-imposing power-law distributions. Results demonstrate that evolved search dynamics are more accurately described by intermittent search than by strict scale-free L\'evy motion across various resource distributions and grid sizes, characterized by short displacements punctuated by occasional longer relocations.
Evolved search in depletable landscapes favors intermittent exploration over scale-free L\'evy walks, revealing a nuanced strategy of local exploitation and relocation.
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 L\'evy 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 L\'evy-walk dynamics. Our results indicate that evolved search is more consistent with intermittent dynamics than with strict scale-free L\'evy 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 L\'evy-walk models. While a L\'evy-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 $\rho$ = 0.15 reproduce this preference for the uniform environment and five L\'evy-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.