Genetic algorithm (GA) is used in a number of fields for effective search of complex spaces, or as a learning algorithm. Based on natural genetics, it feeds on its ability to process large amounts of information very quickly. The performance of a GA is satisfactory in most cases, in that it finds solutions in the neighborhood of the global optimum. Some problems, however, are known to be GA-hard and deceive the GA to converge to sub-optimal solutions. We devise a version of GA through the addition of the “genetic waste”, based on the perceived role of “nonsense codons” (or “noncoding segments”), which alleviates the problem by allowing sufficient diversity in the population. This modification allows the algorithm to change the parameters in each run without external interference. Furthermore, the parameters are independent of the fitness values and are thereby applicable to a wide class of problems.#R##N##R##N#Using Goldberg's[10] minimal deceptive problem, we devise a simple method for setting the initial parameters of a genetic algorithm based on the role of nonsense codons in natural genetics. “Genetic waste” provides random parameters throughout the simulations, enabling the abandonment of trial and error search for the optimal combination of initial parameters and thereby adding credibility to the genetic algorithm as a tool of adaptive search.
['Sonja Novkovic', 'Davor Sverko']
The minimal deceptive problem revisited: the role of “genetic waste”