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 modificatio ns have been suggested to alleviate the difficulties encountered by GA s in solving different problems. Despite these modifications, with the increase in application traditional GAs remain inadequate for many pr actical purposes. This paper introduces a new genetic model called the structured genetic algorithm (sGA) to address some of the difficultie s encountered by the simple genetic approaches in solving various type s of problems. The novelty of this genetic model lies primarily in its redundant genetic material and a gene activation mechanism that utili zes a multilayered structure for the chromosome. This representation p rovides genetic variation and has many advantages in search and optimi zation. 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 ad aptation in non stationary environments. Theoretical arguments and emp irical studies are presented which demonstrate that the sGA can more e fficiently solve complex problems than simple GAs. It is also noted th at the sGA exhibits greater implicit nondisruptive diversity than othe r existing genetic models, while its possession of neutral (apparently redundant) genetic material is consistent with biological systems.