A More biologically motivated genetic algorithm: the model and some results

For more than two decades, genetic algorithms (GAs) have been studied by researchers from different fields. Over the years, many modifications have been suggested to alleviate the difficulties encountered by GAs in solving different problems. Despite these modifications, with the increase in application traditional GAs remain inadequate for many practical purposes. This paper introduces a new genetic model called the structured genetic algorithm (sGA) to address some of the difficulties encountered by the simple genetic approaches in solving various types of problems. The novelty of this genetic model lies primarily in its redundant genetic material and a gene activation mechanism that utilizes a multilayered structure for the chromosome. This representation provides genetic variation and has many advantages in search and optimization. For example, it can retain multiple (alternative) solutions or parameter spaces in its representation. In effect, it also works as a long-term distributed memory within the population, enabling rapid adaptation in non stationary environments. Theoretical arguments and empirical studies are presented which demonstrate that the sGA can more efficiently solve complex problems than simple GAs. It is also noted that the sGA exhibits greater implicit nondisruptive diversity than other exist-ing genetic models, while its possession of neutral (apparently redundant) genetic material is consistent with biological systems. © 1994 Taylor & Francis Group, LLC