Evolving Gene Regulatory Networks for Real Time Control of Foraging Behaviours.
Michał Joachimczak, Borys Wróbel · Artificial Life · 2010
We use a genetic algorithm to obtain artificial gene regulatory networks (GRNs) controlling real time behaviour of artificial agents (animats) that gather food resources in a 2D environment. We build a system in which evolving GRNs are encoded in linear genomes. The encoding allows to determine which transcriptional factors (TFs) interact with which regulatory regions (promoters) to form a GRN. The sensory information is provided to an animat as externally driven concentration of selected TFs. Concentration of selected internally produced TFs is interpreted as signals for actuators. We first consider foraging for one food source and then scale the problem up to obtain animats that are able to switch between two types of food sources and avoid the poisonous one. We show that our system is highly evolvable, even though the genome encoding is very flexible (which results in a large search space) and though continuous product accumulation and degradation causes latencies in signal processing by the networks. We then discuss the topological properties of evolved networks and their evolutionary trajectories. Our results provide a first step toward a more ambitious goal of developing an artificial ecosystem in which multiple individuals will compete for food and mates.