Article thumbnail

Evidence of coevolution in multi-objective evolutionary algorithms

By Dr James M Whitacre


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

Topics: Complexity Theory, Evolution, Artificial Intelligence
Year: 2009
OAI identifier:

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles


  1. (2000). A game-theoretic investigation of selection methods used in evolutionary algorithms,"
  2. (1990). Co-evolving parasites improve simulated evolution as an optimization procedure,"
  3. (1992). Coevolution in a rugged fitness landscape,"
  4. (1991). Coevolution to the edge of chaos: Coupled fitness landscapes, poised states, and coevolutionary avalanches,"
  5. (1976). Evolution and the Theory of Games,"
  6. (2005). Scale-Free Networks Provide a Unifying Framework for the Emergence of Cooperation,"
  7. (2006). Sympatric speciation by self-organizing barriers to gene flow in simulated populations with localized mating,"
  8. (1997). The Design and Analysis of a Computational Model of Cooperative Coevolution,"
  9. (2007). The SelfOrganization of Interaction Networks for Nature-Inspired Optimization "
  10. (2002). Time-dependent extinction rate and species abundance in a tangled-nature model of biological evolution," Physical Review E,