免疫的最適化手法-VLSIフロアプラン設計問題への応用-

抄録

Genetic Algorithms (GAs) are search procedures for combinatorial optimization problems. Because GAs are based on multi-point search and use crossover operator, GAs have an excellent global search ability. However GAs are not effective for searching the solution space locally due to crossover-based-search, and the diversity of the population sometimes decreases rapidly. In order to overcome these drawbacks, we propose a new algorithm called Immunity-based GA (IGA) combining features of the Immune System with GAs. IGA is expected to improve the local search ability of GAs and maintain the diversity of the population. We apply IGA to the VLSI floorplan design problem. Experimental results show that IGA performs better than GAs.