Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Denoising Autoencoder that Guides Stochastic Search   An algorithm is described that adaptively learns a non-linear mutation
distribution. It works by training a denoising autoencoder (DA) online at each
generation of a genetic algorithm to reconstruct a slowly decaying memory of
the best genotypes so far. A compressed hidden layer forces the autoencoder to
learn hidden features in the training set that can be used to accelerate search
on novel problems with similar structure. Its output neurons define a
probability distribution that we sample from to produce offspring solutions.
The algorithm outperforms a canonical genetic algorithm on several
combinatorial optimisation problems, e.g. multidimensional 0/1 knapsack
problem, MAXSAT, HIFF, and on parameter optimisation problems, e.g. Rastrigin
and Rosenbrock functions.

37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex