PROCEEDINGS IPMU '08
An Improved Multi-Objective Genetic Algorithm for Tuning Linguistic Fuzzy Systems
M. J. Gacto, R. Alcalá and F. Herrera
This work proposes the use of Multi-
Objective Evolutionary Algorithms
to obtain Fuzzy Rule-Based Systems
with good accuracy-interpretability
trade-off. To do this, we present
a new post-processing method that
performs rule selection and membership
function tuning by focusing
in the Pareto zone containing the
most accurate solutions but with the
least number of possible rules. This
method is based on the well-known
SPEA2 algorithm, applying an intelligent
crossover operator, considering
some modifications to concentrate
the search in the desired Pareto
zone and including an incest prevention
mechanism in order to obtain
more global optima. The results
show that improving the trade-off
between exploration and exploitation
in the search process enhances
the SPEA2 algorithm performance.
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