Evolving strategies for global optimization - a finite state machine approach
Clemens Prey, Günter R. Leugering · 2001
In this work Genetic Programming methods are used to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. Here 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. Reinterpreting the resulting optimal discrete dynamical system as an algorithm 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 evidence on key properties of resulting strategies.