Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
A rough penalty genetic algorithm for constrained optimization
Information Sciences: an International Journal
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Penalty functions are often used to handle constrained optimization problems in evolutionary algorithms. However, most of the penalty adjustment methods are based on mathematical approaches not on evolutionary ones. To mimic the biological phenomenon of the values judgment, we introduce the rough set theory as a novel penalty adjustment method. Furthermore, a new marriage selection is proposed in this paper to modify the multiple-evaluation genetic algorithm. By applying rough-penalty and marriage-selection methods, the proposed algorithm generally is both effective and efficient in solving several constrained optimization problems. The experimental results also show that the proposed mechanisms further improve and stabilize the solution ability.