- author = "Bob McKay",
- title = "Partial Functions in Fitness-Shared Genetic Programming",
- booktitle = "Proceedings of the 2000 Congress on Evolutionary Computation CEC00",
- year = "2000",
- pages = "349--356",
- volume = "1",
- address = "La Jolla Marriott Hotel La Jolla, California, USA",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "6-9 " # jul,
- organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, fitness, accurate solutions, fitness sharing, multiplexer definition learning, partial functions, performance, population parameters, recursive list membership function learning, total functions, functions, learning (artificial intelligence), list processing, multiplexing equipment, software performance evaluation",
- ISBN = "0-7803-6375-2",
-
DOI = "
doi:10.1109/CEC.2000.870316",
- abstract = "This paper investigates the use of partial functions and fitness sharing in genetic programming. Fitness sharing is applied to populations of either partial or total functions and the results compared. Applications to two classes of problem are investigated: learning multiplexer definitions, and learning (recursive) list membership functions. In both cases, fitness sharing approaches outperform the use of raw fitness, by generating more accurate solutions with the same population parameters. On the list membership problem, variants using fitness sharing on populations of partial functions outperform variants using total functions, whereas populations of total functions give better performance on some variants of multiplexer problems.",
-
notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 00TH8512,
Library of Congress Number = 00-018644",
