The efficiency of a GA is highly dependent on the values of the algorithm's control parameters. Assuming that basic features like the selection procedure are predetermined, the control parameters available for adjustment are:

- the population size
**N**, - the crossover probability , and
- the mutation probability .

De Jong [15] made some recommendations based on his observations of the performance of GAs on a test bed of 5 problems, which included examples with difficult characteristics such as discontinuities, high dimensionality, noise and multimodality. His work suggested that settings of

would give satisfactory performance over a wide range of problems.