A Genetic Algorithm based Approach for Multi-Objective Data-Flow Graph Optimization

Abstract: 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 correct-ness 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 crit-ical path length. We also demonstrate that, due to their flexibility, genetic algorithms can be simply adapted to dif-ferent objective functions which is examplarily shown for power optimization. In order to avoid inferior results caused by the counteracting demands on resources of different ba-sic blocks, all DFGs of the input description are optimized concurrently. Experimental results for several standard benchmarks prove the efficiency of our approach.