Many features of a GA may be varied. The details of this particular implementation of the GA for optimization of biochemical kinetics are:

- Parameters are encoded in genes using floating-point representation, rather than the more usual binary representation.
- Mutation is carried out by adding to the gene a random number drawn from a normal distribution with zero mean and a standard deviation of 10% of the parameter value. Whenever this makes the parameter (gene) exceed one boundary, it is set to that boundary value.
- Cross-over is always performed at gene boundaries so that no gene is ever disrupted. The number of cross-over points is a random number between zero and half the number of adjustable parameters (uniform distribution).
- Selection is done by a tournament where each individual competes with a number of others equal to 20% the population size. The competitors are chosen at random.
- The initial population contains one individual whose genes are the initial parameter values, the genes of all other individuals are initialized to a random value between their boundaries. If the boundaries span two orders of magnitude or more, the random distribution is exponential, otherwise normal.
- Whenever the fittest individual has not changed for the last 10 generations, the 10% less fit individuals are replaced by individuals with random genes. When the fittest individual has not changed for 30 generations, the worse 30% are substituted by individuals with random genes. When the fittest individual has not changed for 50 generations, the worse 50% are substituted by individuals with random genes. This procedure helps the algorithm escape local minima and is somewhat equivalent to increasing the mutation rate when the population has become uniform.

**Number of Generations**- The parameter is a positive integer value to determine the number of generations the algorithm shall evolve the population. The default is '200'.

**Population Size**- The parameter is a positive integer value to determine the size of the population, i.e., the number of individuals that survive after each generation. The default is '20'.

**Random Number Generator**- The parameter is an enumeration value to determine which random number generator this method shall use. COPASI provides two random number generators R250 [Maier91] (selected through the value 0) and the Mersenne Twister [Matsumoto98] (selected through the value 1 (default)).

**Seed**- The parameter is a positive integer value to determine the seed for the random number generator. A value of zero instructs COPASI to select a "random" value.