DocumentCode :
356767
Title :
Partial functions in fitness-shared genetic programming
Author :
McKay, R. I Bob
Author_Institution :
Sch. of Comput. Sci., Australian Defence Force Acad., Canberra, ACT, Australia
Volume :
1
fYear :
2000
fDate :
2000
Firstpage :
349
Abstract :
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 are 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
Keywords :
functions; genetic algorithms; learning (artificial intelligence); list processing; multiplexing equipment; software performance evaluation; accurate solutions; fitness sharing; genetic programming; multiplexer definition learning; partial functions; performance; population parameters; recursive list membership function learning; total functions; Application software; Australia; Computer science; Delay; Drives; Error analysis; Evolutionary computation; Genetic programming; Multiplexing; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Conference_Location :
La Jolla, CA
Print_ISBN :
0-7803-6375-2
Type :
conf
DOI :
10.1109/CEC.2000.870316
Filename :
870316
Link To Document :
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