抄録
Genetic algorithms (GAs) are search procedures for combinatorial optimization problems. Unlike most of other optimization techniques, GAs search the solution space using a population of solutions. Although GAs have an excellent global search ability, it is not effective for searching the solution space locally due to crossover-based-search. Genetic Immune Recruitment Mechanism (GIRM) makes up for the week point of GAs. In GIRM, new generated solutions take an immune recruitment test. Only those solutions which are similar to the best solution in the population pass the test and survive to the next generation. As a result, GIRM promotes the search around the best solution. In this paper we apply GIRM to the floorplan design problem of VLSI layout and compare the results with the ones of GAs. Especially, we propose new affinity functions in order to evaluate the similarity between floorplans. Computational experiments show that GIRM gives equivalent or better solutions than GAs.
