Implications of behavioral architecture for the evolution of self-organized division of labor.

ABSTRACT: Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a pr

SUBMITTER: Duarte A 

PROVIDER: S-EPMC3310710 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature