Article,
Co-Evolution of Finite State Machines for Optimization: Promotion of Devices Which Search Globally
International Journal of Computational Intelligence and Applications, 1 (2): 1--16 (2002)
Abstract
In this work a co-evolutionary approach is used in
conjunction with Genetic Programming operators in order
to find certain transition rules for two-step discrete
dynamical systems. This issue is similar to the
well-known artificial-ant problem. We seek the dynamic
system to produce a trajectory leading from given
initial values to a maximum of a given spatial
functional. This problem is recast into the framework
of input-output relations for controllers, and the
optimization is performed on program trees describing
input filters and finite state machines incorporated by
these controllers simultaneously. In the context of
Genetic Programming there is always a set of test cases
which has to be maintained for the evaluation of
program trees. These test cases are subject to
evolution here, too, so we employ a so-called
host-parasitoid model in order to evolve optimizing
dynamical systems. Reinterpreting these systems as
algorithms for finding the maximum of a functional
under constraints, we have derived a paradigm for the
automatic generation of adapted optimization algorithms
via optimal control. We provide numerical examples
generated by the GP-system MathEvEco. These examples
refer to key properties of the resulting strategies and
they include statistical evidence showing that for this
problem of system identification the co-evolutionary
approach is superior to standard Genetic Programming.
