This paper demonstrates that running a genetic algorithm with injection of individuals from solutions to similar problems can get better performance than running a genetic algorithm with random initialization. We also find that running GAs with information from the original problem or the adding one city problem can get better performance than running GAs with information from other kinds of modified problems. We believe this is because the same problem and the add1 problem contain all cities of the original problem and do not change the length of the edges and are therefore more similar to the original problems.
Although the approach has promise, much work remains to be done. We need to consider the effect of different selection schemes, recombination operators, and niching operators, for genetic search, as well different search algorithms. Deriving more refined estimates on the quality and quantity of individuals to inject will broaden applicability. Individuals need not be injected solely into the initial population. We can keep track of the performance of injected individuals and their progeny and use this information to design and inject individuals in intermediate generations. Finally, the tradeoffs between speed and solution quality needs to be explored in more detail.