Cartesian Genetic Programming as Local Optimizer of Logic Networks

Logic synthesis and optimization methods work either globally on the whole logic network or locally on preselected subnetworks. Evolutionary design methods have already been applied to evolve and optimize logic circuits at the global level. In this paper, we propose a new method based on Cartesian genetic programming (CGP) as a local area optimizer in combinational logic networks. First, a subcircuit is extracted from a complex circuit, then the subcircuit is optimized by CGP and finally the optimized subcircuit replaces the original one. The procedure is repeated until a termination criterion is satisfied. We present a performance comparison of local and global evolutionary optimization methods with a conventional approach based on ABC and analyze these methods using differently pre-optimized benchmark circuits. If a sufficient time is available, the proposed locally optimizing CGP gives better results than other locally operating methods reported in the literature; however, its performance is significantly worse than the evolutionary global optimization.