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14 results for “Neuroevolution”
Figure 5. A chromosome structure in case we have 2 visible states and 3 invisible states-Neuroevolution Mechanism for Hidden Markov Model
<p>Generating a population of size n of HMMs at random can be performed with some<br> restrictions:<br> - The weights representing the input layer in the chromosome should be always negligible as<br> initial values.<br> - The weights which are involved in summation of 1.0 in the hidden layer part of the<br> chromosome should be exactly 1.0.<br> Let us assume the following case<br> Visible states are 2 and invisible states (observations) are 3, , then we shall have a<br> chromosome as shown in Figure 5.</p>
Figure 6. Two point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
<p>Two point Crossover<br> For the two point crossover we get two parent HMMs and choose at random two cutting points for<br> the weights that have a sum of 1.0 and swap the contents between the crossing points. This is<br> illustrated in the example shown in Figure 6.</p>
Figure 4. A chromosome structure for HMM shown in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>The chromosome which represents the HMM can be extracted from its corresponding neural<br> network. The general structure of the chromosome is divided into two sections, input layer and<br> hidden layer. Each section contains many slots, and each slot represents a weight from one node in<br> that layer to a node in the next layer (from input to hidden and from hidden to output). The number<br> of slots in the input layer is the same number of input nodes in the neural network. In the hidden<br> layer, number of slots is equal to nodes in the output layer multiplied by the nodes in the hidden<br> layer.</p>
Figure 3. Neural network representation for HMM given in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>In our proposed structure, we injected a hidden layer to have a multilayer perceptron which<br> is more efficient than single layer perceptron.<br> To make this process clear, Figure 3 shows the neural networks for the HMM presented in<br> Figure 2.</p>
Figure 9. Adding-Reducing mutation.-Neuroevolution Mechanism for Hidden Markov Model
<p>Adding a small value from one weight and decrement that value to another weight. The chosen<br> weights should be involved in summation of 1.0. Figure 9 shows an example, we add 0.001 from<br> one weight and decrement the same value from another weight.</p>
Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Neuroevolution Mechanism for Hidden Markov Model
<p>The advantage of using this technique is that MCPRs are very useful in real time<br> applications and can be adapted over time based on the obtained experience of the networking<br> working process. Again Hewahi[6] proposed a mechanism (algorithm) to evolve and select the best<br> suitable HMM for a given problem using GA, this mechanism lacks to the training process that can<br> be of great usefulness in finding the best HMM.<br> Based on the above mentioned research, the importance of using HMM is increasing<br> rapidly.<br> Let us consider the HMM presented in Figure 1.</p>
Figure 2. HMM with weights and necessary conditions on top of edges-Neuroevolution Mechanism for Hidden Markov Model
<p>Based on the HMM structure in Figure 2, we can perform the following steps:<br> 1. Make the number of nodes of inputs in the input layer of the NN as the number of states<br> (visible states not the observations). Each input node represents one state.<br> 2. Number of nodes in the output layer in the NN is equal to the number of states and<br> observations (visible and invisible states), where each node corresponds to one state (visible<br> or invisible).<br> 3. We construct a hidden layer in NN with n number of nodes, where n is the same number of<br> nodes in the input layer.<br> 4. We make a connection from every input to every hidden layer node with a very negligible<br> weight.<br> 5. Connect every hidden node in the hidden layer to every node in the output layer.<br> 6. Assign weights from the hidden layer to output layer in a way that as every node in the<br> hidden layer corresponding to input state. The weight on top of the link between the hidden<br> node to the output node is the probability value between the states in the HMM.<br> In our proposed structure, we injected a hidden layer to have a multilayer perceptron which<br> is more efficient than single layer perceptron.<br> To make this process clear, Figure 3 shows the neural networks for the HMM presented in<br> Figure 2.</p>
Figure 11. Group mutation-Neuroevolution Mechanism for Hidden Markov Model
<p>This happens by swapping two complete groups with summation of 1.0 with the same criteria.<br> Figure 10 shows a case of this.</p>
Figure 7. One point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
This crossover is performed in the input layer part only. We choose a crossing cut point in the input layer part of the chromosome, and exchange everything before it. This is illustrated in Figure 7.
Data for "Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra"
<p>This record contains neuroevolution potential (NEP) and tensor neuroevolution potential (TNEP) models (nep*.txt) for molecular water species, liquid water as well as barium zirconate, along with training data<i> (*</i>.zip). The models were constructed using GPUMD 3.9 (https://gpumd.org/).</p>
Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution
<p>Replication package for the paper "Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution" </p><p> </p><p>Although automated test generation is common in many programming domains, games still challenge test generators due to their heavy randomisation and hard-to-reach program states. Neuroevolution combined with search-based software testing principles has been shown to be a promising approach for testing games, but the co-evolutionary search for optimal network topologies and weights involves unreasonably long search durations. Humans, on the other hand, tend to be quick in picking up basic gameplay. In this paper, we therefore aim to improve the evolutionary search for game input generators by integrating knowledge about human gameplay behaviour. To this end, we propose a novel way of systematically recording human gameplay traces, and integrating these traces into the evolutionary search for networks using traditional gradient descent as a mutation operator. Experiments conducted on eight diverse Scratch games demonstrate that the proposed approach reduces the required search time from five hours down to only 30 minutes on average.</p>
Figure 8. Exchange mutation.-Neuroevolution Mechanism for Hidden Markov Model
<p>Exchange two neighbor weights involved in a summation of 1.0. This is illustrated in Figure 8.</p>
Large-scale simulation of thermal conductivity in CaSiO3 perovskite with neuroevolution potential
<p>The dataset contains the thermal conductivity of CaSiO3 perovskit, MgSiO3 perovskite, periclase, as well as the heat flux across the core-mantle boundary.</p>
Neuroevolution potential for aluminum
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