Bubbleworld.Evo: Artificial Evolution of Behavioral Decisions in a Simulated Predator-Prey Ecosystem

  • Thomas Schmickl
  • Karl Crailsheim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


This article presents a multi-agent simulation of an abstract ecosystem which is inhabited by two species: a predator species and a prey species. Both species show the typical behaviors found in such an ecological relationship that are: hunting behavior and escaping behavior. In the simulation, the actors make behavioral decisions according to “genetically fixed” weighting parameters. These parameters determine which prey item is selected by the predator and which predators are avoided the most by prey. Thus these parameters shape the decisions performed by both species. We incorporated artificial evolution by allowing successful animals to pass their features to their offspring, a process that includes mutation and recombination of these “genes”. The simulation shows that different kinds of optimal behavioral choices emerge out of artificial evolution, when the simulation is run with different physiological and morphological parameters of the actors.


State Unit Intraspecific Competition Artificial Life Prey Animal Behavioral Decision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thomas Schmickl
    • 1
  • Karl Crailsheim
    • 1
  1. 1.Department for ZoologyKarl-Franzens-University GrazGrazAustria

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