  Over the years, several meta-heuristic algorithms were proposed and are now
emerging as common methods for constrained optimization problems. Among them,
genetic algorithms (GA's) shine as popular evolutionary algorithms (EA's) in
engineering optimization. Most engineering design problems are difficult to
resolve with conventional optimization algorithms because they are highly
nonlinear and contain constraints. In order to handle these constraints, the
most common technique is to apply penalty functions. The major drawback is that
they require tuning of parameters, which can be very challenging. In this
paper, we present a constraint-handling technique for GA's solely using the
violation factor, called VCH (Violation Constraint-Handling) method. Several
benchmark problems from the literature are examined. The VCH technique was able
to provide a consistent performance and match results from other GA-based
techniques.
