Genetic stigmergy

Joshua Brandoff, Hiroki Sayama

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Stigmergy has long been studied and recognized as an effective system for selforganization among social insects. Through the use of chemical agents known as pheromones, insect colonies are capable of complex collective behavior often beyond the scope of an individual agent. In an effort to develop human-made systems with the same robustness, scientists have created artificial analogues of pheromone-based stigmergy, but these systems often suffer from scalability and complexity issues due to the problems associated with mimicking the physics of pheromone diffusion.

Original languageEnglish
Title of host publicationBio-Inspired Self-Organizing Robotic Systems
Pages81-103
Number of pages23
Volume355
DOIs
Publication statusPublished - 2011
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume355
ISSN (Print)1860-949X

Fingerprint

Scalability
Physics

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Brandoff, J., & Sayama, H. (2011). Genetic stigmergy. In Bio-Inspired Self-Organizing Robotic Systems (Vol. 355, pp. 81-103). (Studies in Computational Intelligence; Vol. 355). https://doi.org/10.1007/978-3-642-20760-0_4

Genetic stigmergy. / Brandoff, Joshua; Sayama, Hiroki.

Bio-Inspired Self-Organizing Robotic Systems. Vol. 355 2011. p. 81-103 (Studies in Computational Intelligence; Vol. 355).

Research output: Chapter in Book/Report/Conference proceedingChapter

Brandoff, J & Sayama, H 2011, Genetic stigmergy. in Bio-Inspired Self-Organizing Robotic Systems. vol. 355, Studies in Computational Intelligence, vol. 355, pp. 81-103. https://doi.org/10.1007/978-3-642-20760-0_4
Brandoff J, Sayama H. Genetic stigmergy. In Bio-Inspired Self-Organizing Robotic Systems. Vol. 355. 2011. p. 81-103. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-642-20760-0_4
Brandoff, Joshua ; Sayama, Hiroki. / Genetic stigmergy. Bio-Inspired Self-Organizing Robotic Systems. Vol. 355 2011. pp. 81-103 (Studies in Computational Intelligence).
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