In this paper we present a new tool for the encoding of multi-level finite state machines based on the concept of
evolution programming. Evolution programs are stochastic adaptive algorithms, based on the paradigm of genetic
algorithms whose search methods model some natural phenomenon: genetic inheritance and Darwinian strife for
survival. Crossover and mutation rates were tailored to the state assignment problem experimentally. We present
results over a wide range of MCNC benchmarks which demonstrate the effectiveness of the new tool. The results
show that evolution programs can be effectively applied to state assignment.