Predicting population dynamics and evolutionary trajectories based on performance evaluations in alife simulations

ABSTRACT

Evolutionary investigations are often very expensive in terms of the required computational resources and many general questions regarding the utility of a feature F of an agent (e.g., in competitive environments) or the likelihood of F evolving (or not evolving) are therefore typically difficult, if not practically impossible to answer. We propose and demonstrate in extensive simulations a methodology that allows us to answer such questions in setups where good predictors of performance in a task T are available. These predictors evaluate the performance of an agent kind A in a task T*, which can then transformed by including costs and additional factors to make predictions about the performance of A in T.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Multiagent systems

Additional Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.0 General

General Terms:
Experimentation, Performance

Keywords:
a-life, adaptive behavior, agents, evolution