Evolutionary algorithms and Particle Swarm Optimization for artificial language evolution

Abstract

This paper reports upon two adaptive approaches for deriving words in an artificial language simulation. The efficacy of a Particle Swarm Optimization (PSO) method versus an Artificial Evolution (AE) method was examined for the purpose of adapting communication between agents. The objective of the study was for agents to derive a common (shared) lexicon for talking about food resources in the simulation environment. In the simulation, communication was essential for agent survival and as such facilitated lexicon adaptation. Results indicated that PSO was effective at adapting agents to quickly converge to a common lexicon, where, on average, one word for each food type was derived. AE required more method iterations to converge to a common lexicon that contained, on average, multiple words for each food type. However, there was greater word diversity in the lexicon converged upon by AE evolved agents, compared to that converged upon by PSO adapted agents.