Genetic VLSI circuit partitioning with dynamic embedding

Abstract

This paper suggests a new genetic algorithm (GA) for 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 encodings themselves undergo evolution. Before generating every new solution, we first generate 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 which are 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 parents solutions, a fitness value is assigned to the underlying encoding. The encoding is discarded or enter the pool based on the fitness. Two populations are maintained for this purpose: one for solutions and the other for diverse encodings. On experiments with the public ACM/SIGDA benchmark circuits, the new genetic algorithm significantly outperformed recently published state-of-the-art approaches.