A rough penalty genetic algorithm for constrained optimization

Many real-world issues can be formulated as constrained optimization problems and solved using evolutionary algorithms with penalty functions. To effectively handle constraints, this study hybridizes a novel genetic algorithm with the rough set theory, called the rough penalty genetic algorithm (RPGA), with the aim to effectively achieve robust solutions and resolve constrained optimization problems. An infeasible solution is subjected to rough penalties according to its constraint violations. The crossover operation in the genetic algorithm incorporates a novel therapeutic approach and a parameter tuning policy to enhance evolutionary performance. The RPGA is evaluated on eleven benchmark problems and compared with several state-of-the-art algorithms in terms of solution accuracy and robustness. The performance analyses show this approach is a self-adaptive method for penalty adjustment. Remarkably, the method can address a variety of constrained optimization problems even though the initial population includes infeasible solutions.