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Abstract
After a brief introduction to fuzzy sets and fuzzy rules, I describe how Ant
Colony Optimization (ACO) may be used for inducing a fuzzy rule from
data. A common strategy for inducing a complete rule set is iterative rule
learning, where a rule discovery mechanism, in this case an ACO algorithm, is
run several times in succession. The result of each ACO algorithm - the best
fuzzy rule as determined by some quality function - is considered a partial
solution. I compare this iterative strategy against one that runs several ACO
algorithms simultaneously, and illustrate why this second strategy is a more
viable approach for fuzzy rule induction.
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