如何标准化遗传算法的神经网络？
我正在尝试使用遗传算法训练一个简单的前馈神经网络，但事实证明它效率相当低，因为同构神经网络看起来与遗传算法不同。
可以有多个神经网络，它们的行为方式相同，但其神经元从左到右和跨级别以不同的方式排序。对于遗传算法来说，这些网络的基因型将显得完全不同。因此，任何交叉尝试都是毫无意义的，遗传算法最终会像爬山一样有效。
您能否推荐一种标准化网络的方法，以便它们对遗传算法显得更加透明？
I am trying to train a simple feedforward neural network using a genetic algorithm, however it is proving fairly inefficient because isomorphic neural networks appear different to the genetic algorithm.
It is possible to have multiple neural networks, which behave the same way, but have their neurons ordered in a different way from left to right and across levels. To the genetic algorithms those networks' genotypes will appear completely different. Therefore any attempt to do crossover is pointless and the GA ends up being as effective as hill climbing.
Can you recommend a way to normalize the networks so they appear more transparent to the genetic algorithm?
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在这种情况下，我认为交叉是“低效的”，而不是“毫无意义的”。解决您提到的重复的一种方法可能是以某种规范顺序对隐藏层神经元进行排序，并在交叉期间使用此顺序，这至少可以减少隐藏权重空间中遇到的重复。
此外，您可以通过比遗传算法更直接的方法来拟合输出层权重。您没有说明正在使用什么性能指标，但许多常见的度量都有相当简单的优化。因此，举个例子，您可以使用遗传算子生成一个新的隐藏层，然后通过逻辑回归拟合输出层，并让 GA 评估整个网络。
I would call crossover in this context "inefficient", rather than "pointless". One way to address the duplication you mention might be to sort the hidden layer neurons in some canonical order, and use this order during crossover, which might at least reduce the duplication encountered in hidden weight space.
Also, you might fit the output layer weights by a more direct method than genetic algorithms. You don't say what performance metric is being used, but many common measures have fairly straightforward optimizations. So, as an example, you might generate a new hidden layer using genetic operators, then fit the output layer by logistic regression, and have the GA evaluate the total network.