The most easily observed ant behavior is workers foraging for food. Foraging workers do not eat the food, but carry it back to the nest, where it is processed and consumed by all members of the colony. In many species, a high degree of coordination and cooperation between foragers is observed (usually mediated by pheromone communication). We would like to understand more about the evolution of cooperative foraging. In this paper, we describe a computer program called AntFarm, that simulates the evolution of foraging strategies in colonies of artificial organisms that resemble ants. AntFarm is work in progress, and is being used to investigate issues surrounding simulated evolution of complex behaviors in complex environments, the evolution of cooperation among closely related individuals, and the evolution of chemical communication. We describe our genetic algorithm for simulating evolution. We also discuss the issue of the representation of artificial organisms, and empirically compar...

One of the main goals of the researchers in the field of artificial life is to increase our understanding of natural life. We are particularly interested in using artificial life to study issues in natural evolution and population genetics. Current tools of population geneticists and evolutionary biologists are inherently limited. For example, only the simplest genetic systems can be completely understood analytically, the fossil record is incomplete and difficult to interpret, and evolutionary experiments in the laboratory or field are usually limited to at most a few dozen generations and are difficult to control and repeat. Simulated evolution makes it possible to study evolutionary systems over thousands of generations (macroevolution) and on large populations. In this paper, we demonstrate that microanalytic (low--level) computer simulations of evolving populations of artificial organisms can usefully augment analytic population genetics models. We begin with a simple analytical m...