Genetic programming and co-evolution with exogenous fitness in an artificial life environment

The study of artificial life involves simulating biological or sociological processes with a computer. Combining artificial life with techniques from evolutionary computation frequently involves modeling the behavior or decision processes of artificial organisms within a society in such a way that genetic algorithms can be applied to modify these models and enhance behavior over time. Typically, endogenous fitness is used with co-evolution. We explore the use of an exogenous fitness function with genetic programming and co-evolution to develop individuals and species capable of competing in a hostile environment. To facilitate the study, we use a commercially available environment-AI Wars-to host the organisms and run the experiments. Results from our experiments, though preliminary, indicate the ability of co-evolution, genetic programming, and exogenous fitness to evolve fit individuals. The results also suggest the ability to assess the nature of the fitness landscape and the impact of various fitness factors on evolutionary performance