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Performance Evaluation of Best-Worst Selection Criteria for Genetic Algorithm

机译:遗传算法最差选择准则的性能评估

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Genetic algorithm's performance is based on three major factors, which are selection criteria, crossover and mutation operators. Each factor has its own significant role but the selection criteria to choose parents from the population is the key role to running the genetic algorithm. There is a number of selection schemes that have been introduced in literature and all have their own advantages. Most of the selection criterion is chose the parents which give highly optimum values based on the theory that healthy parents produce healthy offspring. In the current study, we proposed a new selection scheme which selects healthy parents as well as unhealthy parents. The novel selection scheme is simple to implement, and it has notable ability to reduce the effected of premature convergence compared to other selection schemes. We apply this new technique along with some traditional selection schemes on six benchmark problems and Simulation studies show a remarkable performance of the proposed selection scheme.
机译:遗传算法的性能基于三个主要因素,即选择标准,交叉和变异算子。每个因素都有其重要作用,但是从种群中选择亲本的选择标准是运行遗传算法的关键作用。文献中已经介绍了许多选择方案,它们都有各自的优势。大多数选择标准都是根据健康父母产生健康后代的理论选择具有最佳价值的父母。在当前的研究中,我们提出了一个新的选择方案,可以选择健康的父母和不健康的父母。该新颖的选择方案易于实现,并且与其他选择方案相比,具有显着的减少过早收敛的影响的能力。我们将这项新技术与一些传统的选择方案一起应用于六个基准问题,并且仿真研究表明,所提出的选择方案具有出色的性能。

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