Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A new adaptive penalty scheme for genetic algorithms
Information Sciences: an International Journal - Special issue: Evolutionary computation
A new proposal for multi-objective optimization using differential evolution and rough sets theory
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
Computers and Operations Research
A rough set penalty function for marriage selection in multiple-evaluation genetic algorithms
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects
Information Sciences: an International Journal
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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.