A Constraint-Handling Technique for Genetic Algorithms using a Violation Factor
This work addresses constraint-handling in genetic algorithms for engineering design problems, but it is incremental as it builds on existing GA methods without major breakthroughs.
The paper tackles the challenge of handling constraints in genetic algorithms for engineering optimization by introducing a Violation Constraint-Handling (VCH) method that uses a violation factor instead of penalty functions. The result shows that VCH provides consistent performance and matches results from other GA-based techniques on benchmark problems.
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.