Nonstationary Function Optimization using the Structured Genetic Algorithm

In this paper, we describe the application of a new type of genetic algorithm called the Structured Genetic Algorithm (sGA) for function optimization in nonstationary environments. The novelty of this genetic model lies primarily in its redundant genetic material and a gene activation mechanism which utilizes a multi-layered structure for the chromosome. In adapting to nonstationary environments of a repeated nature genes of long-term utility can be retained for rapid future deployment when favourable environments recur. The additional genetic material preserves optional solution space and works as a long term distributed memory within the population structure. This paper presents important aspects of sGA which are able to exploit the repeatability of many nonstation-ary function optimization problems. Theoretical arguments and empirical study suggest that sGA can solve complex problems more eeciently than has been possible with simple GAs. We also noted that sGA exhibits implicit genetic diversity and viability as in biological systems.