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 en-vironments. 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 environ-ments 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 eciently than has been possible with sim-ple GAs. We also noted that sGA exhibits implicit genetic diversity and viability as in biological systems. 1. Introduction