Time dependent optimization with a folding genetic algorithm

Time-dependent optimization has revealed to be a promising gap for the entire genetic algorithms community since it has numerous applications. This paper extends previous work (Collard et al., 1996) related to the use of meta-genes in the so-called dual genetic algorithms (DGAs). A more generic framework, involving a variable number of genes, is introduced. Folding genetic algorithms are thus proposed as a new class of genetic algorithms, whose effectiveness is investigated on two well-known models of dynamical environments and compared to simple genetic algorithms and DGAs. Eventually, further analysis of these results enlightens the ability of FGAs to evolve a metric over the search space (i.e. a kind of encoding scheme) along with potential solutions. These particularly encouraging results open up interesting perspectives, as FGAs could be applied to to other fundamental problems investigated by the genetic algorithms community in order to measure the benefits of this really meta-level of evolution