192 research outputs found
Neuron - synapse level problem decomposition method for cooperative neuro - evolution of feedforward networks for time series prediction
A major concern in cooperative coevolution for neuro-evolution is the appropriate problem decomposition method that takes into account the architectural properties of the neural network. Decomposition to the synapse and neuron level has been proposed in the past that have their own strengths and limitations depending on the application problem. In this paper, a new problem decomposition method that combines neuron and synapse level is proposed for feedfoward networks and applied to
time series prediction. The results show that the proposed approach has improved the results in selected benchmark data sets when compared to related methods. It also has promising performance when compared to other computational intelligence methods from the literature
Enhancing competitive island cooperative neuro - evolution through backpropagation for pattern classification
Cooperative coevolution is a promising method for training neural networks which is also known as cooperative neuro-evolution. Cooperative neuro-evolution has been used for pattern classification, time
series prediction and global optimisation problems. In the past, competitive island based cooperative coevolution has been proposed that employed different instances of problem decomposition methods for competition. Neuro-evolution has limitations in terms of training time although they are known as global search methods. Backpropagation algorithm employs gradient descent which helps in faster convergence which is needed for neuro-evolution. Backpropagation suffers from premature convergence and its combination with neuro-evolution can help eliminate the weakness of both the approaches. In this paper, we propose a competitive island cooperative neuro-evolutionary method that takes advantage of the strengths of gradient descent and neuro-evolution. We use feedforward neural networks on benchmark pattern classification problems to evaluate the performance of the proposed algorithm. The results show
improved performance when compared to related methods
Competitive two - island cooperative co - evolution for training feedforward neural networks for pattern classification problems
In the application of cooperative coevolution for
neuro-evolution, problem decomposition methods rely on architectural properties of the neural network to divide it into subcomponents. During every stage of the evolutionary process, different problem decomposition methods yield unique characteristics that may be useful in an environment that enables solution sharing. In this paper, we implement a two-island competition environment in cooperative coevolution based neuro-evolution for
feedforward neural networks for pattern classification problems. In particular the combinations of three problem decomposition methods that are based on the architectural properties that refers to neural level, network level and layer level decomposition. The experimental results show that the performance of the competition method is better than that of the standalone problem decomposition cooperative neuro-evolution methods
Problem Decomposition and Adaptation in Cooperative Neuro-Evolution
One way to train neural networks is to use evolutionary algorithms
such as cooperative coevolution - a method that decomposes the network's
learnable parameters into subsets, called subcomponents. Cooperative
coevolution gains advantage over other methods by evolving particular
subcomponents independently from the rest of the network. Its success
depends strongly on how the problem decomposition is carried out.
This thesis suggests new forms of problem decomposition, based on a
novel and intuitive choice of modularity, and examines in detail at what
stage and to what extent the different decomposition methods should be
used. The new methods are evaluated by training feedforward networks
to solve pattern classification tasks, and by training recurrent networks to
solve grammatical inference problems.
Efficient problem decomposition methods group interacting variables
into the same subcomponents. We examine the methods from the literature and provide an analysis of the nature of the neural network optimization problem in terms of interacting variables. We then present a
novel problem decomposition method that groups interacting variables
and that can be generalized to neural networks with more than a single
hidden layer.
We then incorporate local search into cooperative neuro-evolution. We
present a memetic cooperative coevolution method that takes into account
the cost of employing local search across several sub-populations.
The optimisation process changes during evolution in terms of diversity and interacting variables. To address this, we examine the adaptation
of the problem decomposition method during the evolutionary process. The results in this thesis show that the proposed methods improve performance
in terms of optimization time, scalability and robustness.
As a further test, we apply the problem decomposition and adaptive
cooperative coevolution methods for training recurrent neural networks
on chaotic time series problems. The proposed methods show better performance
in terms of accuracy and robustness
Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction
Cooperative coevolution decomposes a problem into subcomponents and employs evolutionary algorithms for solving them. Cooperative coevolution has been effective for evolving neural networks. Different problem decomposition methods in cooperative coevolution determine how a neural network is decomposed and encoded which affects its performance. A good problem decomposition method should provide enough diversity and also group interacting variables which are the synapses in the neural network. Neural networks have shown promising results in chaotic time series prediction. This work employs two problem decomposition methods for training Elman recurrent neural networks on chaotic time series problems. The Mackey-Glass, Lorenz and Sunspot time series are used to demonstrate the performance of the cooperative neuro-evolutionary methods. The results show improvement in performance in terms of accuracy when compared to some of the methods from literature
Modified Neuron-Synapse level problem decomposition method for Cooperative Coevolution of Feedforward Networks for Time Series Prediction
Complex problems have been solved efficiently through decomposition of a particular problem using problem decompositions. Even combination of different distinct problem decomposition methods has shown good results in time series prediction. The application of different problem decomposition methods at different stages of a network can share its strengths to solve the problem in hand better. Hybrid versions of two distinct problem decomposition methods has showed promising results in past. In this paper, a modified version of latterly introduced Neuron-Synapse level problem decomposition is proposed using feedforward neural networks for time series prediction. The results shows that the proposed modified model has got better results in more datasets when compared to its previous version. The results are better in some cases for proposed method in comparison to several other methods from the literature
Memetic cooperative coevolution of Elman recurrent neural networks
Cooperative coevolution decomposes an optimi-
sation problem into subcomponents and collectively solves
them using evolutionary algorithms. Memetic algorithms
provides enhancement to evolutionary algorithms with local
search. Recently, the incorporation of local search into a
memetic cooperative coevolution method has shown to be
efficient for training feedforward networks on pattern classification problems. This paper applies the memetic cooperative coevolution method for training recurrent neural networks on grammatical inference problems. The results show
that the proposed method achieves better performance in
terms of optimisation time and robustness
Multi - objective cooperative neuro - evolution of recurrent neural networks for time series prediction
Cooperative coevolution is an evolutionary computation method which solves a problem by decomposing it into smaller subcomponents. Multi-objective optimization
deals with conflicting objectives and produces multiple optimal solutions instead of a single global optimal solution. In previous work, a multi-objective cooperative co-evolutionary method was introduced for training feedforward neural networks on time series problems. In this paper, the same method is used for training recurrent neural networks. The proposed approach is
tested on time series problems in which the different time-lags represent the different objectives. Multiple pre-processed datasets distinguished by their time-lags are used for training and testing. This results in the discovery of a single neural network that can correctly give predictions for data pre-processed using different
time-lags. The method is tested on several benchmark time
series problems on which it gives a competitive performance in comparison to the methods in the literature
Cooperative neuro - evolution of Elman recurrent networks for tropical cyclone wind - intensity prediction in the South Pacific region
Climate change issues are continuously on the rise
and the need to build models and software systems for management of natural disasters such as cyclones is increasing. Cyclone wind-intensity prediction looks into efficient models to forecast the wind-intensification in tropical cyclones which can be used as a means of taking precautionary measures. If the wind-intensity is determined with high precision a few hours prior, evacuation and further precautionary measures can take place. Neural networks have become popular as efficient tools for forecasting. Recent work in neuro-evolution of Elman recurrent neural network showed promising performance for benchmark problems. This paper employs Cooperative Coevolution method for training Elman recurrent neural networks for Cyclone wind- intensity prediction in the South Pacific region. The results show very promising performance in terms of prediction using different parameters in time series data reconstruction
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