title: Optimal Fungal Space Searching Algorithms.
abstract: Previous experiments have shown that fungi use an efficient natural algorithm for searching the space available for their growth in micro-confined networks, e.g., mazes. This natural "master" algorithm, which comprises two "slave" sub-algorithms, i.e., collision-induced branching and directional memory, has been shown to be more efficient than alternatives, with one, or the other, or both sub-algorithms turned off. In contrast, the present contribution compares the performance of the fungal natural algorithm against several standard artificial homologues. It was found that the space-searching fungal algorithm consistently outperforms uninformed algorithms, such as Depth-First-Search (DFS). Furthermore, while the natural algorithm is inferior to informed ones, such as A*, this under-performance does not importantly increase with the increase of the size of the maze. These findings suggest that a systematic effort of harvesting the natural space searching algorithms used by microorganisms is warranted and possibly overdue. These natural algorithms, if efficient, can be reverse-engineered for graph and tree search strategies.
authors.forename: Elitsa
authors.initials: E
authors.lastname: Asenova
authors.forename: Hsin-Yu
authors.initials: HY
authors.lastname: Lin
authors.forename: Eileen
authors.initials: E
authors.lastname: Fu
authors.forename: Dan V
authors.initials: DV
authors.lastname: Nicolau
authors.forename: Dan V
authors.initials: DV
authors.lastname: Nicolau
mesh_terms: D000465:Algorithms; D032701:Biomimetics / Q000379:methods*; D005658:Fungi / Q000502:physiology*; D046210:Microfluidic Analytical Techniques / Q000295:instrumentation*; D008954:Models, Biological