An immunity based genetic algorithm and its application to the VLSI floorplan design problem

The genetic algorithm (GA) paradigm is a search procedure for combinatorial optimization problems. Unlike most of other optimization techniques, GA searches the solution space using a population of solutions. Although GA has an excellent global search ability, it is 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 (IS) with GA. The proposed method is expected to have local search ability and prevent premature convergence. We apply IGA to the floorplan design problem of VLSI layout. Experimental results show that IGA performs better than GA