基于DNN的可训练交叉的遗传算法作为一般搜索的部分专业化的例子
Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search
摘要：
普遍归纳法依赖于某种一般的搜索过程，这种过程注定是低效的。实现通用性和效率的一种可能性是将这个过程专门化。任何特定的狭窄任务。然而，完全的专业化，意味着直接从任务参数映射到解决方案（判别模型），而不搜索并不总是可能的。在本文中，一般搜索的部分专业化被认为是遗传算法（GAs）的形式与一个专门的交叉算子。我们执行这种想法的可行性研究，实现这样的运营商的形式，一个深前馈神经网络。将具有可训练交叉算子的GA与完全专业化的结果进行比较，完全专业化的结果也表示为深度神经网络。实验结果表明，专门的遗传算法可以更有效地比一般的遗传算法和判别模型。
英文摘要：
Universal induction relies on some general search procedure that is doomed to be inefficient. One possibility to achieve both generality and efficiency is to specialize this procedure w.r.t. any given narrow task. However, complete specialization that implies direct mapping from the task parameters to solutions (discriminative models) without search is not always possible. In this paper, partial specialization of general search is considered in the form of genetic algorithms (GAs) with a specialized crossover operator. We perform a feasibility study of this idea implementing such an operator in the form of a deep feedforward neural network. GAs with trainable crossover operators are compared with the result of complete specialization, which is also represented as a deep neural network. Experimental results show that specialized GAs can be more efficient than both general GAs and discriminative models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
