Soft multiple expression and genetic redundancy: preliminary results for non-stationary function optimization

Many real world problems are dynamic in nature, and they deal with changing environments or objective functions. Dynamic objective functions can make the evolutionary search tedious or unsuccessful for Genetic Algorithms. Some work has focused on altering the evolutionary process, including the selection strategy, genetic operators, replacement strategy, or fitness modification. While other work focused on the concept of genotype to phenotype mapping or gene expression. This line of work includes models based on diploidy and dominance, messy GAs, proportional GA, overlapping genes such as in DNA coding method, the floating point representation, and the structured GA. In particular, the structured GA uses a simple structured hierarchical chromosome representation, where lower level genes are collectively switched on or off by specific higher level genes. Genes that are switched on are expressed into the final phenotype, while genes that are switched off do not contribute to coding the phenotype. We have recently proposed a modification of the sGA based on the concept of soft activation mechanism. The lower level genes are no longer limited to total expression or to none. Instead, they can be expressed to different gradual degrees. The soft structured Genetic Algorithm (s/sup 2/GA) inherits all the advantages of its crisp (non-fuzzy) counterpart (sGA), and possesses several additional unique features compared to the sGA and other GA based techniques. In this paper, we empirically demonstrate several strengths of the S/sup 2/GA approach with regard to non-stationary objective function optimization.