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GA has a reputation for being durable, meaning that it can usually deliver an overhaul of the initial design. But, for a particular design domain, they could not be the correct solution. A hybridization approach should be used to try to make the most of all the worlds to maximize their convergence rates in situations when more information is available and is not generated randomly (for example, where gradient details are available). Typically, a hill-climbing algorithm is used in the genetic code to allow everyone in the group to climb on the local hill. The system also encourages each offspring to climb a local hill, created at the breeding stage.

While the simple convergence of GA search algorithms associated with hybrid approaches is the common meaning, this term can also be used for a less straightforward hybridization, where GA and gradient search methods are employed in sequence. Use the GA to reverse the optimizing problem and then deliver the output to the conventional optimizer from this first stage to complete the operation. The first design approach would design the right initial layout for GA before moving on to the full design level, where the second stage optimization phase will begin with the use of classical search techniques. This can also be found in MDO implementations.