A genetic algorithm for VLSI channel routing in the presence of cyclic vertical constraints

An efficient merger of the channel routing theory and the genetic stochastic search introduces a new paradigm for VLSI channel routing. The feasibility of the paradigm is demonstrated by the first genetic router for Manhattan model channels which contain cyclic vertical constraints. The router has been implemented as a self contained genetic algorithm system. It creates an initial population without violations of constraints using the results of a new theory of locally optimal breaking of cyclic vertical constraints. The population size is problem instance dependent to provide for the preservation of the structural diversity which is built into the initial population. A new encoding scheme and novel crossover and mutation operators are introduced. Convergence rates to optimal solutions are excellent.