99,407 research outputs found
Learning to Generate Genotypes with Neural Networks
Neural networks and evolutionary computation have a rich intertwined history. They most commonly appear together when an evolutionary algorithm optimises the parameters and topology of a neural network for reinforcement learning problems, or when a neural network is applied as a surrogate fitness function to aid the evolutionary optimisation of expensive fitness functions. In this paper we take a different approach, asking the question of whether a neural network can be used to provide a mutation distribution for an evolutionary algorithm, and what advantages this approach may offer? Two modern neural network models are investigated, a Denoising Autoencoder modified to produce stochastic outputs and the Neural Autoregressive Distribution Estimator. Results show that the neural network approach to learning genotypes is able to solve many difficult discrete problems, such as MaxSat and HIFF, and regularly outperforms other evolutionary techniques
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
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Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning
Using a model heat engine, we show that neural network-based reinforcement
learning can identify thermodynamic trajectories of maximal efficiency. We
consider both gradient and gradient-free reinforcement learning. We use an
evolutionary learning algorithm to evolve a population of neural networks,
subject to a directive to maximize the efficiency of a trajectory composed of a
set of elementary thermodynamic processes; the resulting networks learn to
carry out the maximally-efficient Carnot, Stirling, or Otto cycles. When given
an additional irreversible process, this evolutionary scheme learns a
previously unknown thermodynamic cycle. Gradient-based reinforcement learning
is able to learn the Stirling cycle, whereas an evolutionary approach achieves
the optimal Carnot cycle. Our results show how the reinforcement learning
strategies developed for game playing can be applied to solve physical problems
conditioned upon path-extensive order parameters
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