- author = "Michael Waters and John Sheppard",
- title = "Genetic Programming and Co-Evolution with Exogenous Fitness in an Artificial Life Environment",
- booktitle = "Proceedings of the Congress on Evolutionary Computation",
- year = "1999",
- editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala",
- volume = "3",
- pages = "1641--1648",
- address = "Mayflower Hotel, Washington D.C., USA",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "6-9 " # jul,
- organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, coevolution, AI Wars, artificial life environment, artificial organisms, co-evolution, commercially available environment, decision processes, endogenous fitness, evolutionary computation, evolutionary performance, exogenous fitness, fitness factors, fitness function, fitness landscape, hostile environment, artificial life, competitive algorithms, decision theory",
- ISBN = "0-7803-5536-9 (softbound)",
- ISBN = "0-7803-5537-7 (Microfiche)",
-
URL = "
http://citeseer.ist.psu.edu/waters99genetic.html",
-
DOI = "
doi:10.1109/CEC.1999.785471",
- abstract = "The study of artificial life involves simulating biological or sociological processes with a computer. Combining artificial life with techniques from evolutionary computation frequently involves modelling the behaviour 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 coevolution, 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",
-
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
