A Mapping-Based Convex Constraint-Handling Technique for Evolutionary Algorithms

This paper proposes a new mapping-based constraint-handling technique for Evolutionary Algorithms (EAs) applied to linearly constrained optimization problems in which the feasible region is given by a convex hull. The search space of EAs is usually given by a hyper-cube. The proposed technique called Convex-Hull (CH) mapping transforms a real vector in the search space of EA to a solution in the feasible region. Therefore, CH-mapping makes any EAs applicable directly to linearly constrained optimization problems. The performance of Differential Evolution (DE) using CH-mapping is evaluated on test problems and a real-world application, namely the portfolio optimization problem.