An Investigation of Fitness Sharing in Genetic
Programming
The Australian Journal of Intelligent Information
Processing Systems, 7 (1/2):
43--51 (July 2001)Abstract
This paper investigates fitness sharing in genetic
programming. Implicit fitness sharing is applied to
populations of programs. Three treatments are compared:
raw fitness, pure fitness sharing, and a gradual change
from fitness sharing to raw fitness. The 6- and
11-multiplexer problems are compared. Using the same
population sizes, fitness sharing shows a large
improvement in the error rate for both problems.
Further experiments compare the treatments on learning
recursive list membership functions; again, there are
dramatic improvements in error rate. Conversely,
fitness sharing runs achieve comparable results to raw
fitness using populations two to three times smaller.
Measures of population diversity suggest that the
results are due to preservation of diversity and
avoidance of premature convergence by the fitness
sharing runs.