Crossover rate - the crossover rate is the probability that the crossover operator will be applied to a particular chromosome during a generation.  These probabilities range from 0 to 1.  This factor should be set fairly high, and the default of .9 is usually good.

Mutation rate - the mutation rate is the probability that the mutation operator will be applied to a particular chromosome during a generation.  The probability ranges from 0 to 1, and should be set fairly low.  Mutation rates in the range .001 to .05 are usually reasonable, with the default of .01 being good for a wide range of problems.

Generation Gap - It is sometimes desirable that individuals in the population be allowed to "live" longer and go into the next generation.  This is especially true if the individuals selected to live are the most fit ones in the population.  The fraction of those that do NOT go into the next generation is called the Generation Gap.  It is specified as a fraction from 0.0 to 1.0.  For example, if the Generation Gap is set at .98, then only 2 percent will go directly into the next generation without being subject to crossover and mutation, and 98 percent will die after possibly reproducing.  If the population size is 50, then .02 * 50=1 individuals will be sent to the next generation.  If Elitism is on, then the individuals sent directly to the next generation will always be the most fit individuals.  Otherwise, they will be randomly selected.  Set the Generation Gap so that only one or two individuals go into the next generation.

Elitism - When the Generation Gap is set (see previous section), some individuals live into the next generation after they breed or produce mutations.  With Elitism, the individuals who live are the most fit (elite) individuals.  Elitism should almost always be on so that the most desirable genetic characteristics are available for breeding in subsequent generations.  In some cases, it is helpful to start GeneHunter with the Elitism option off to allow the population to evolve without significant selective pressure.  After a while, you may turn the Elitism option on in order to concentrate the optimization process around the best solutions.

Diversity - Diversity is a form of slight mutation, producing individuals that are only slightly changed, as opposed to the major changes in individuals that the mutation operator can produce.  For some problems, Diversity can make a major contribution to evolutionary progress; in others it will not make much difference at all.  However, it should be utilized in most cases.  Diversity has been called “creep” in other genetic algorithm implementations.