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Differential Evolution (DE) is one of the most powerful global numerical optimization algorithms in the field of evolutionary algorithm. However, the performance of DE is affected by control parameters and mutation strategies. In addition, the choice of the control parameters and mutation strategies is strongly dependent on the characteristics of optimization problems. As a result, studies focused...
Differential evolution, one of the evolutionary algorithms, is a population-based stochastic search technique for solving optimization problems in a continuous space. Due to its simplicity, effectiveness and robustness, DE has been applied to a variety of real-world problems. As one of the successful application, DE is incorporated to interactive evolutionary computation (IEC) framework. Interactive...
Differential Evolution (DE) is one of the evolutionary algorithm that was developed to handle optimization problems over continuous domains. It's a population-based stochastic search technique with simple concept and high efficient. In recent year, many DE variants were proposed and have been applied for solving various problems. In addition, some DE based techniques are modified to handle discrete...
Differential evolution (DE) is a simple yet efficient evolutionary algorithm. Because of its simplicity, effectiveness and robustness, DE has gradually become more popular and applied in various fields. In addition, a lot of works have been done to improve the search ability of DE. Among them, opposition-based DE (ODE), which is incorporated opposition-based learning (OBL), has shown better performance...
Differential evolution (DE) is one of the evolutionally algorithms for solving optimization problems in a continuous space. DE has been widely applied to solve various optimization problems. Additionally, many modified DE algorithms have been developed in an attempt to improve search performance. In this paper, we propose island-based DE with varying subpopulation size. Island model is one of the...
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