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Universal induction relies on some general search procedure that is doomed tobe inefficient. One possibility to achieve both generality and efficiency is tospecialize this procedure w.r.t. any given narrow task. However, completespecialization that implies direct mapping from the task parameters tosolutions (discriminative models) without search is not always possible. Inthis paper, partial specialization of general search is considered in the formof genetic algorithms (GAs) with a specialized crossover operator. We perform afeasibility study of this idea implementing such an operator in the form of adeep feedforward neural network. GAs with trainable crossover operators arecompared with the result of complete specialization, which is also representedas a deep neural network. Experimental results show that specialized GAs can bemore efficient than both general GAs and discriminative models.

Genetic algorithms with DNN-based trainable crossover as an example of partial specialization of general search