Evolution of Adaptive Population Control in Multi-agent Systems

Abstract

Dynamic population management is an important aspect of multi-agent systems. In artificial immune systems, for example, a shortage of agents can lead to undetected threats, while an overabundance of agents can degrade quality of service and if unchecked, even create new vulnerabilities. Unfortunately, designing an effective strategy for population management is complicated by the myriad of possible circumstances and environmental conditions the agents may face after deployment. In this paper, we present the results of a study in applying digital evolution to the population management problem. In digital evolution, populations of self-replicating computer programs evolve in a user-defined computational environment, where they are subject to mutations and natural selection. Our results demonstrate that populations of digital organisms are capable of evolving self-adaptive replication behaviors that respond to attack fluctuations, as well as clever strategies for cooperating to mitigate attacks. This study provides evidence that digital evolution may be a useful tool in the design of self-organizing and self-adaptive agent-based systems.