Guiding Designs of Self-Organizing Swarms: Interactive and Automated Approaches
Neural and Evolutionary Computing
2017-05-29 v1 Adaptation and Self-Organizing Systems
Abstract
Self-organization of heterogeneous particle swarms is rich in its dynamics but hard to design in a traditional top-down manner, especially when many types of kinetically distinct particles are involved. In this chapter, we discuss how we have been addressing this problem by (1) utilizing and enhancing interactive evolutionary design methods and (2) realizing spontaneous evolution of self organizing swarms within an artificial ecosystem.
Keywords
Cite
@article{arxiv.1308.3400,
title = {Guiding Designs of Self-Organizing Swarms: Interactive and Automated Approaches},
author = {Hiroki Sayama},
journal= {arXiv preprint arXiv:1308.3400},
year = {2017}
}
Comments
23 pages, 16 figures, 3 tables