Summary
We employ a genetic algorithm to
automatically generate a fuzzy process controller. Unique to
this approach is that each controller is represented by an
unordered list of an arbitrary number of rules. We establish the efficacy of a specialized "add rule" and "delete rule" mutation operators, and propose a mechanism by which rule base size evolves.
A critical aspect of this
methodology is the emergence of an appropriately-sized
rule base as a result of the genetic search.
The approach
produces a representation of the underlying fuzzy structure more
parsimonious than with prior examples of genetic search in which lists of parameters evolve under a fixed set of membership
functions, or tables of output values evolve to a fixed set of
physical system configurations.
In addition, while an
appropriate action may be strongly conditioned by a subset
of the control variables, techniques based upon solution
space partitioning require that every output be dependent
upon every input. We overcome this limitation with
a mechanism through which the consequent of a rule may
be calculated solely from relevant variables.
The method has been successfully applied to several difficult
control problems, including the classical cart-pole benchmark,
boat steering, and aircraft landing.