It is often effective to combine the genetic algorithm technique and other local optimizations or heuristic improvements. This can be done within the GA procedure by incorporating a local optimization into a user objective function and returning an improved objective value. Either your user objective function can replace the original solution with the optimized one, or you can leave the solution unchanged, replacing it with the optimized one only at the final iteration.

Replacing the original solution with the locally optimized one speeds convergence, but it also increases the risk of converging prematurely. If you choose to do so, you can modify the solution by writing the changed solution back to the population with a WriteMember call. You could also consider replacing the original solution with some probability p. For some problems, values of p from 0.05 to 0.15 have been shown to significantly improve convergence while avoiding premature convergence to a local optimum. This technique is illustrated in Example 3.1.