Practical optimization problems often come with constraints, which may make the problem difficult to solve. Constraints are handled in GAs in a variety of ways.
If it is possible, the most straightforward approach is to set the problem encoding, genetic operators, and initialization such that the constraints are automatically met. For example, a nonlinear optimization problem over n real variables with constant upper and lower bounds is easily formulated in IML using real fixed-length encoding, arithmetic crossover, and uniform mutation. The arithmetic crossover operator can be used without modification in any optimization over a convex solution space, when the optimum is expected to be an interior point of the domain.
Another approach to satisfying constraints is to repair solutions after genetic operators have been applied. This is what IML does when using the heuristic crossover operator or delta mutation operator with fixed bounds: it adjusts any individual component that violates an upper or lower bound. You can repair a solution inside a user crossover or mutation module, or repairs can be made by modifying the solution in a user objective function module, as was described in the previous section.
Another technique is to allow solutions to violate constraints, but to impose a penalty in the objective function for unsatisfied constraints. If the penalty is severe enough, the algorithm should converge to an optimum point within the constraints. This approach should be used carefully. If most of the points in the solution space violate the constraints, then this technique may converge prematurely to the first feasible solution found. Also, convergence may be poor to a solution that lies on or near a constraint boundary.