A Random Walker Can Optimize the Exploration without the Large Capacity Memory

Tomoko Sakiyama

2021

Abstract

A random walker explores an unknown field and sometimes changes its movement property using new spatial information obtained by it during its exploration. An important matter is the relation between the movement property of a random walker and the use for acquired information. I recently developed a random walk model in which a walker coordinated its directional rule based on its experiences and found that this model presented an optimal random walk, which demonstrated a so-called Lévy walk with μ = 2.00. Here, I investigate the foraging efficiency for that model and verify whether a large memory capacity is required or not in order to maintain the foraging efficiency. My findings reveal that the proposed model can apply to biological processes where a random walker does not have a high memory capacity.

Paper Citation

in Harvard Style

Sakiyama T. (2021). A Random Walker Can Optimize the Exploration without the Large Capacity Memory.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: Paradigms-Methods-Approaches, ISBN 978-989-758-490-9, pages 209-212. DOI: 10.5220/0010369902090212

in Bibtex Style

@conference{paradigms-methods-approaches21,
author={Tomoko Sakiyama},
title={A Random Walker Can Optimize the Exploration without the Large Capacity Memory},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: Paradigms-Methods-Approaches,},
year={2021},
pages={209-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010369902090212},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: Paradigms-Methods-Approaches,
TI - A Random Walker Can Optimize the Exploration without the Large Capacity Memory
SN - 978-989-758-490-9
AU - Sakiyama T.
PY - 2021
SP - 209
EP - 212
DO - 10.5220/0010369902090212