The discovery that the True Slime Mold Physarum Polycephalum, an amoeba-like unicellular organism, can solve the shortest path problem in mazes (news article) has recently served as an inspiration for a new shortest path algorithm, termed Physarum Solver (paper). While this algorithm is theoretically interesting, its computational complexity limits its practical usefulness. Unlike other nature-inspired algorithms, such asAnt Colony Optimization or Genetic Algorithms, Physarum Solver does not use stochastic sampling to reduce the problem space. We will explore whether it is possible to extend Physarum Solver in this form and how this compares to other nature-inspired search methods.