Dynamic embedding for genetic VLSI circuit partitioning

Abstract

This paper suggests a new genetic algorithm (GA) for a VLSI circuit-partitioning problem. In a genetic algorithm, the encoding of a solution plays an important role. The key feature of the new genetic algorithm is a technique to provide, dynamically, many encodings, in which the encodings themselves undergo evolution. Before generating each new solution, it first generates a new encoding by combining two encodings chosen from a pool containing diverse encodings. The new solution is generated by a crossover which combines two parent solutions temporarily encoded by the generated encoding scheme. That is, a new solution is generated by a two-layered crossover. Depending on the new solution's quality and its improvement over the parent solutions, a fitness value is assigned to the underlying encoding. Two populations are maintained for this purpose: one for the solutions and the other for the encodings. In addition to the dynamic encoding, genes' geographical linkages are effectively utilized by converting linear encodings to two-dimensional encodings for more effective genetic space searching. On experiments with the public ACM/SIGDA benchmark circuits, the new genetic algorithm significantly outperformed recently published state-of-the-art approaches.