Evidence of coevolution in multi-objective evolutionary algorithms

TL;DR

This paper shows that coevolutionary dynamics can occur in multi-objective evolutionary algorithms under simple conditions, even without direct species interactions, influenced by interaction-based fitness measures and environmental changes.

Contribution

It broadens understanding of coevolution in multi-objective optimization and reveals that direct species interactions are not necessary for coevolution to occur.

Findings

Coevolution occurs with interaction-based fitness measures in multi-objective environments.

Environmental perturbations can trigger coevolutionary processes.

Weaker conditions than previously thought are sufficient for coevolution.

Abstract

This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution.