This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of noncoding segments on both of these representations is studied. Noncoding segments are a computational model of noncoding deoxyribonucleic acid, and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. Genetic algorithms are able to maintain a more diverse population with the floating representation. The combination of noncoding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities.

The genetic algorithm (GA) is a problem-solving method that is modeled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of noncoding segments on GA performance. Noncoding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on noncoding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article we discuss our hypotheses on noncoding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of noncoding segments.