Once crossover and mutation have occurred, the new population members will replace the old ones if they are better. Program users typically want to maximize or minimize the fitness, so the survival of the fittest action is governed by whether the new fitness is better or worse than the previous generation's fitness. The resulting process yields a steadily improving solution to the problem, often identifying the optimum solution in a surprisingly short time. What distinguishes Generator from other commercial genetic algorithm software is that in Generator , users can fine-tune these parameters on the fly. In addition, Generator has special hill climbing features, recombination operators and mate selection processes to rapidly find the best solution.
Generator begins working on a problem by creating a random population of trial solutions.
A population of up to 100 members can be specified, although the default population of 20 members usually works best. This allows the trial solutions to be ordered according to their fitness. Population members with the best fitness are given the most slots on the roulette wheel. The process is repeated so that each population member selects a mate. Generator then decides how much information is swapped between the different population members.