Salzburg Interest Group on Integrated Systems Ptgas { Genetic Algorithms Evolving Non{coding Segments by Means of Promoter/terminator Sequences Ptgas { Genetic Algorithms Evolving Non{coding Segments by Means of Promoter/terminator Sequences

In this article we present work on chromosome structures for Genetic Algorithms (GAs) based on biological principles. Mainly, the in-uence of non{coding segments on GA behavior and performance is investigated. We compare representations with non{coding sequences at predeened, xed locations with \junk" code induced by the use of Pro-moter/Terminator sequences (ptGA) deening start and end of a coding sequence, respectively. As one of the advantages of non{coding segments a few researchers have identiied the reduction of the disruptive eeects of Crossover, and we solidify this argument by a formal analysis of crossover disruption probabilities for non{coding segments at xed locations. The additional use of promoter/terminator sequences not only enables evolution of parameter values, but also allows for adaptation of number, size, and location of genes (problem parameters) on an artiicial chromosome. Randomly generated chromosomes of xed length carry diierent numbers of promoter/terminator sequences resulting in genes of varying size and location. Evolution of these ptGA chromosomes drives the number of parameters and their values to (sub)optimal solutions. Moreover, the formation of tightly linked building blocks is enhanced by self{organization of gene locations. We also introduce a new, non{disruptive crossover operator emerging from the ptGA gene structure with adaptive crossover rate, location and number of crossover sites. For experimental comparisons of this genetic operator to conventional crossover in GAs, as well as properties of diierent ptGA chromosome structures, an artiicial problem from the literature is utilized. Finally, the potential of ptGA is demonstrated on an NP{complete combinatorial optimization problem.