Adaptive Self-Organizing Organisms Using a Bio-Inspired Gene Regulatory Network Controller

Abstract

This work has explored the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behavior. Using an Alife simulation framework that mimics a changing environment, we have shown that separating the static from the conditionally active part of the network contributes to a better adaptive behavior. This chapter describes the biologically inspired concept of GRNs to develop a distributed robot self-organizing approach. In particular, it shows that by using this approach, multiple swarm robots can aggregate to form a robotic organism that can adapt its configuration as a response to a dynamically changing environment. In addition, through the comparison of several different simulation experiments, the results illustrate the impact of evolutionary operators such as mutations and duplications on improving the adaptability of organisms.