A genetic algorithm based approach for multi-objective data-flow graph optimization

This paper presents a genetic algorithm based approach for algebraic optimization of behavioral system specifications. We introduce a chromosomal representation of data-flow graphs (DFG) which ensures that the correctness of algebraic transformations realized by the underlying genetic operators selection, recombination, and mutation is always preserved. We present substantial fitness functions for both the minimization of overall resource costs and critical path length. We also demonstrate that, due to their flexibility, genetic algorithms can be simply adapted to different objective functions which is shown for power optimization. In order to avoid inferior results caused by the counteracting demands on resources of different basic blocks, all DFGs of the input description are optimized concurrently. Experimental results for several standard benchmarks prove the efficiency of our approach.