- author = "Peter J. Angeline and Jordan B. Pollack",
- title = "Competitive Environments Evolve Better Solutions for Complex Tasks",
- year = "1993",
- booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93",
- editor = "Stephanie Forrest",
- publisher = "Morgan Kaufmann",
- pages = "264--270",
- month = "17-21 " # jul,
- address = "University of Illinois at Urbana-Champaign",
- publisher_address = "2929 Campus Drive, Suite 260, San Mateo, CA 94403, USA",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.demo.cs.brandeis.edu/papers/icga5.pdf",
-
URL = "
http://www.demo.cs.brandeis.edu/papers/icga5.ps.gz",
-
URL = "http://www.natural-selection.com/Library/1993/icga93.ps.Z",
- size = "7 pages",
- abstract = "In the typical genetic algorithm experiment, the fitness function is constructed to be independent of the contents of the population to provide a consistent objective measure. Such objectivity entails significant knowledge about the environment which suggests either the problem has previously been solved or other non-evolutionary techniques may be more efficient. Furthermore, for many complex tasks an independent fitness function is either impractical or impossible to provide. In this paper, we demonstrate that competitive fitness functions, i.e. fitness functions that are dependent on the constituents of the population, can provide a more robust training environment than independent fitness functions. We describe three differing methods for competitive fitness, and discuss their respective advantages.",
- ISBN = "1-55860-299-2",
-
notes = "very like thesis
One method I investigated was called competitive fitness functions which is a fitness function that compares performance between members of the population to determine a ranking of individuals for reproduction. THis obviates the need for a quantitative model of the quality of solutions and replaces it with a more simplistic measure of {"}x is better than y{"}. The paper explores this concept using GLiB and appeared in ICGA93.",
