An evolutionary optimization based on the immune system and its application to the VLSI floor-plan design problem

Abstract

Genetic algorithms (GAs) are search procedures for combinatorial optimization problems. Because GAs are based on multipoint search and use the crossover operator, they 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 to maintain the diversity of the population. We apply IGA to the VLSI floor-plan design problem. Experimental results show that IGA performs better than GAs.