Evolved search patterns favor intermittent moves in limited resource grids
Evolutionary foraging in grids: Intermittent search dynamics emerge in finite, depletable landscapes
Neural and Evolutionary Computing
Summary
This paper looks at how animals or agents might search for food in environments where food is limited and doesn’t come back once eaten. The authors let computer-simulated agents evolve movement strategies instead of forcing specific patterns like random long jumps. They find that these agents settle on search behaviors that involve lots of short steps with occasional longer moves, rather than continuous movement patterns called Lévy walks. This approach better fits real situations where resources get used up, supporting the idea of local searching interrupted by longer moves to new areas.
What this means in practice
- •For roboticists: Develop robotic search algorithms to efficiently explore resource-limited environments by emulating intermittent local search with occasional longer moves.
- •For wildlife conservation teams: Model animal movement in depletable habitats more realistically by applying evolved intermittent search patterns to understand foraging behavior.
Authors
Shailendra Bhandari, Alex Szorkovszky, Anis Yazidi, Pedro G. Lind
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.