An Evolutionary Optimization Based on the Immune System and its Application to the VLSI Floor-Plan Design Problem

摘要

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 popula- tion sometimes decreases rapidly. In order to overcome these drawbacks, we propose a new algorithm called immu- nity-based GA (IGA), combining features of the immune system with GAs.