Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimization of Evolutionary Neural Networks Using Hybrid Learning
  Algorithms   Evolutionary artificial neural networks (EANNs) refer to a special class of
artificial neural networks (ANNs) in which evolution is another fundamental
form of adaptation in addition to learning. Evolutionary algorithms are used to
adapt the connection weights, network architecture and learning algorithms
according to the problem environment. Even though evolutionary algorithms are
well known as efficient global search algorithms, very often they miss the best
local solutions in the complex solution space. In this paper, we propose a
hybrid meta-heuristic learning approach combining evolutionary learning and
local search methods (using 1st and 2nd order error information) to improve the
learning and faster convergence obtained using a direct evolutionary approach.
The proposed technique is tested on three different chaotic time series and the
test results are compared with some popular neuro-fuzzy systems and a recently
developed cutting angle method of global optimization. Empirical results reveal
that the proposed technique is efficient in spite of the computational
complexity.

2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph