Preventing Premature Convergence to Local Optima in Genetic Algorithms via Random Offspring Generation

摘要: The Genetic Algorithms (GAs) paradigm is being used increasingly in search and optimization problems. method has shown to be efficient robust a considerable number of scientific domains, where the complexity cardinality problems considered elected themselves as key factors taken into account. However, there are still some insufficiencies; indeed, one major usually associated with use GAs premature convergence solutions coding local optima objective function. problem tightly related loss genetic diversity GA’s population, cause decrease on quality found. Out question, this fact lead development different techniques aiming solve, or at least minimize problem; traditional methods work maintain certain degree target populations, without affecting process GA. In one’s work, these compared an innovative one, Random Offspring Generation, presented evaluated its merits. Traveling Salesman Problem benchmark.