The study of living systems has taken many forms, from research into fundamental physical processes to ethological studies of animal behavior on a global scale. Traditionally these investigations have focused exclusively on “real” biological systems existing in our world’s ecological system. Only recently have investigations of living systems begun to occur in “artificial” systems in computers and robotic hardware.

The potential benefits of an enhanced understanding of living systems are tremendous. Some are of a grand scale and are intuitively obvious, such as improvements in our ability to manage our own real ecosystems, the development of true machine intelligence, and the possibility of understanding our own mental and physiological processes. Some are of a more prosaic scale, but more accessible thereby, and perhaps of more immediate utility, such as simple learning systems, robust pattern classifiers, general purpose optimization schemes, robotic controllers, and evolvable software algorithms. The technological issues of the study of Artificial Life (ALife) are well laid out by Langton [27] in the proceedings of the first ALife workshop; the societal and philosophical implications of ALife are well presented by Farmer and Belin [16] in the proceedings of the second ALife workshop. This paper discusses a computer model of living organisms and the ecology they exist in called PolyWorld. PolyWorld attempts to bring together all the principle components of real living systems into a single artificial (man-made) living system. PolyWorld brings together biologically motivated genetics, simple simulated physiologies and metabolisms, Hebbian learning in arbitrary neural network architectures, a visual perceptive mechanism, and a suite of primitive behaviors in artificial organisms grounded in an ecology just complex enough to foster speciation and inter-species competition. Predation, mimicry, sexual reproduction, and even communication are all supported in a straightforward fashion. The resulting survival strategies, both individual and group, are purely emergent, as are the functionalities embodied in their neural network “brains”. Complex behaviors resulting from the simulated neural activity are unpredictable, and change as natural selection acts over multiple generations.