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A Novel Search Biases Selection Strategy for Constrained Evolutionary Optimization

机译:约束进化优化的新型搜索偏向选择策略

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The issues of the search biases selection based on stochastic ranking are pointed out by an example with three possible outputs and are also demonstrated by an experiment designed here. In order to improve the explicit search biases ability in feasible regions, three conditions for explicit search biases are presented and a novel search biases selection strategy with stochastic ranking is proposed in this paper. This strategy is applied to our new algorithm based on ES (Evolution Strategy). The new algorithm has been tested on 13 common benchmark functions and the experimental results have demonstrated that to some extent the convergence speed, the numerical accuracy and stability of best solutions are improved.
机译:通过具有三个可能输出的示例指出了基于随机排名的搜索偏向选择问题,并且还通过此处设计的实验进行了演示。为了提高在可行区域内的显式搜索偏见能力,提出了三种显式搜索偏见条件,提出了一种具有随机排序的新颖的搜索偏见选择策略。该策略被应用于我们基于ES的新算法(进化策略)。该算法在13个常用基准函数上进行了测试,实验结果表明,该算法在一定程度上提高了收敛速度,提高了最优解的数值精度和稳定性。

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