Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “genetic algorithm model”
A Linked Application of Discrete Differential Evolution Algorithm Coupled with Simulation- Optimization Model and Comparative Analysis by Genetic Algorithm for Discrete Groundwater Management Problems
<p>Complete dataset of publication name as "The complete publication dataset is "A Discrete Differential Evolution- Linear Programming Algorithm for Groundwater Management Problems." You can find all the written codes in the zip file.</p>
Figure 6. One chromosome from the population and the five chromosomes existing in the evaluation partition.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>For example comparing the<br> chromosome given in Figure 6 with the first chromosome in the evaluation partition, the<br> difference between the relation Med-Med and Med-High as a pair is 0.0 and the difference<br> between the relation High-High and High-Med as a pair is 0.1. Similarly the difference between<br> the relation Med-Cold and Med-Hot as a pair is 0.1 and the difference between the relation<br> High-Cold and High-Hot as a pair is 0.2. We sum all these differences to get the value of<br> compare(i,j), the sum value is 0+0.1+0.1+0.2 = 0.4. Using the same approach we compute the<br> compare function with the other four chromosomes and we get values 0.4, 0.5,0.4 and 0.6. Now<br> we sum the five values 0.4 + 0.4 + 0.5+ 0.4 +0.6 = 2.3. The fitness value is then 1/ 2.3 = 0.434.<br> The highest is the fitness value, the better is the performance of the chromosome.</p>
Figure 4. Incorrect crossover operation. The High-High and High-Med probability values summation should be 1.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>In this genetic operator, we choose two chromosomes at random and apply crossover between<br> them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be<br> noted that the crossing cut site should be even number. We should have two crossing cut sites. If<br> we make crossing cut site odd number, the resultant child will not have a correct value of<br> probability. The incorrect crossover is shown in Figure 4.</p>
Figure 5. Mutation process. This is happened by decreasing 0.2 from Med-Cold probability and adding 0.2 to Med- Hot.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Figure 5 illustrates an example of mutation process. In Figure 5, Med-Cold:0.9 and Med-Hot:0.1<br> before mutation and become Med-Cold:0.7 and Med-Hot:0.3 after mutation. This is done by<br> decreasing 0.2 from Med-Cold probability and adding 0.2 to Med-Hot probability.</p>
Figure 3. The crossover operation between two HMM chromosomes Figure-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Crossover<br> In this genetic operator, we choose two chromosomes at random and apply crossover between<br> them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be<br> noted that the crossing cut site should be even number. We should have two crossing cut sites. If<br> we make crossing cut site odd number, the resultant child will not have a correct value of<br> probability. The incorrect crossover is shown in Figure 4.</p>
Figure 2. The general structure of the proposed approach-Genetic Algorithms Principles Towards Hidden Markov Model
<p>The chromosome contains 8 genes, each is represented by the relation between two states<br> accompanied with a probability value. The genes should be formed in this way because this is<br> important in the crossover operation as to be explained later. The most important thing is that each<br> two genes has the probability summation of 1.0. For example Med-Med:02 and Med-High:08 have<br> the summation of 1.0. Similarly High-High:0.6 and High-Med:0.4 have the summation of 1.0. Each<br> two genes with summation of 1.0 should be neighbors.</p>
Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Hewahi [4] presented a modified version of Censored Production Rule (CPR) called<br> Modified Censored Production Rules (MCPR). CPR is proposed by Michalski and Winston [6 ] to<br> capture real time situations. MCPR can fit with hidden Markov model and present a scheme to<br> compute the certainty values of the obtained conclusions out of the induced rules. To compute the<br> certainty values for the rule actions (conclusions), the approach exploited only the probability<br> values associated with the hidden Markov model without using any of the other well known<br> certainty computation approaches. Hewahi [3] also proposed an intelligent networking<br> management system based on the induced MCPRs extracted from a networking structure based on<br> HMM. 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.<br> Let us consider the HMM presented in Figure 1.</p>
Genetic algorithm-based personalized models of human cardiac action potential
<p>We present a novel modification of genetic algorithm (GA) which determines personalized parameters of cardiomyocyte electrophysiology model based on set of experimental human action potential (AP) recorded at different heart rates. In order to find the steady state solution, the optimized algorithm performs simultaneous search in the parametric and slow variables spaces. We demonstrate that several GA modifications are required for effective convergence. Firstly, we used a mutation operator, based on Cauchy amplitude distribution along with a random direction in the parametric space. Secondly, relatively large number of elite organisms (6-10 % of the population passed on to new generation) was required for effective convergence. Test runs with synthetic AP as input data indicate that algorithm error is low for high amplitude ionic currents (1.6±1.6% for IKr, 3.2±3.5% for IK1, 3.9±3.5% for INa, 8.2±6.3% for ICaL). Experimental signal-to-noise ratio above 28 dB was required for high quality GA performance. GA was validated against optical mapping recordings of human ventricular AP and mRNA expression profile of donor hearts. In particular, GA output parameters were rescaled proportionally to mRNA levels ratio between patients. We have demonstrated that mRNA-based models predict the AP waveform dependence on heart rate with high precision. The latter also provides a novel technique of model personalization that makes it possible to map gene expression profile to cardiac function. </p>
Dataset: A phase field model combined with genetic algorithm for polycrystalline hafnium zirconium oxide ferroelectrics
<p>The folder includes generated data MATLAB scripts to read/plot the polarization-electric field (PE) hysteresis curves. The dataset contains phase field generated polycrystalline grain structure, simulated domain structures during polarization reversal, and symmetric PE curves (measured and simulated).</p> <p><strong>Polycrystalline grain structures</strong>: The output files are in the *.txt format, readable by MTEX to generate orientation maps.<br> Column(1) Column(2) Column(3) Column(4) Column(5)<br> X Y φ(rad) θ(rad) 𝜓(rad)<br> ... ... ... ... ...<br> ... ... ... ... ...<br> ... ... ... ... ...</p> <p><strong>Domain structures</strong>: The output files are in the *.csv format, which can be visualized by programs like ParaView.<br> Column(1) Column(2) Column(3) Column(4)<br> X Y Z P<br> ... ... ... ...<br> ... ... ... ...<br> ... ... ... ...</p> <p><br> <strong>PE curves</strong>: The output files are in the *.txt format, readable by MATLAB.<br> Column(1) Column(2)<br> E(MV/cm) P(μC/cm²)<br> ... ...<br> ... ...<br> ... ...</p> <p><strong>List of datasets:-</strong><br> Fig. 1: pecurves/calib_func1.txt (Calibrated p(e)), pecurves/measpe_hf50.txt (Measured PE curve), pecurves/simpe_calib.txt (Simulated PE curve).<br> Fig. 2(a): polcr_struc/xy_col.txt (XY top view), polcr_struc/yz_col.txt (YZ side view), polcr_struc/xz_col.txt (ZX side view)<br> Fig. 2(b): pecurves/simpe_gaopt.txt (Simulated PE curve), pecurves/measpe_hf50.txt (Measured PE curve).<br> Fig. 3: pecurves/calib_func.txt (Calibrated p(e)), pecurves/gaopt_func.txt (GA optimized p(e)).<br> Fig. 5: pecurves/simpe_gaopt.txt (Case 1), pecurves/simpe_elast.txt (Case 2).<br> Fig. 4(e): dom_struc/dom_profile1.csv, (f) dom_struc/dom_profile2.csv, (g) dom_struc/dom_profile3.csv, (h) dom_struc/dom_profile4.csv, (m) dom_struc/dom_profile5.csv, (n) dom_struc/dom_profile6.csv, (o) dom_struc/dom_profile7.csv, (p) dom_struc/dom_profile8.csv<br> Fig. 6: pecurves/simpe_gaopt.txt (GA fit coefficients), pecurves/simpe_ldc1.txt (Set 1), pecurves/simpe_ldc2.txt (Set 2).<br> Fig. 7(a): pecurves/simpe_gaopt.txt (𝝂₀ = 1.0), pecurves/simpe_fr80.txt (𝝂₀ = 0.8), pecurves/simpe_fr50.txt (𝝂₀ = 0.5).<br> Fig. 7(b): pecurves/measpe_hf50.txt (Hf₀.₅Zr₀.₅O₂), pecurves/measpe_hf75.txt (Hf₀.₇₅Zr₀.₂₅O₂).<br> Fig. 8: pecurves/simpe_fr38.txt (Simulated PE curve), pecurves/measpe_hf75.txt (Measured PE curve).<br> Fig. 9: pecurves/simpe_gaopt.txt (Random non-textured), pecurves/simpe_tex001.txt ([001] fiber textured), pecurves/simpe_tex111.txt ([111] fiber textured).<br> Fig. 10(a): polcr_struc/xy_equ.txt (XY top view), polcr_struc/yz_equ.txt (YZ side view), polcr_struc/xz_equ.txt (ZX side view)<br> Fig. 10(b): pecurves/simpe_colmor.txt (Columnar grain microstructure), pecurves/simpe_equmor.txt (Equiaxed grain microstructure).</p> <p><strong>List of MATLAB scripts:</strong><br> Fig 1: matlab_scripts/fig1.m<br> Fig 2(b): matlab_scripts/fig2b.m<br> Fig 3: matlab_scripts/fig3.m<br> Fig 5: matlab_scripts/fig5.m<br> Fig 6: matlab_scripts/fig6.m<br> Fig 7(a): matlab_scripts/fig7a.m<br> Fig 7(b): matlab_scripts/fig7b.m<br> Fig 8: matlab_scripts/fig8.m<br> Fig 9: matlab_scripts/fig9.m<br> Fig 10(b): matlab_scripts/fig10b.m<br> </p>
Genetic algorithm-based personalized models of human cardiac action potential
Open the record for dataset details and reuse information.
Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm
<p>Data from the Paper: Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm.</p> <p>Abstract: </p> <p>Marine ecosystem models are important to identify the processes that affects for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximated an exemplary marine ecosystem model by different artificial neural networks. We used a fully connected network, then applied the sparse evolutionary training (SET) procedure, and finally applied a genetic algorithm (GA) to optimize both the network topology. With all three approaches, a direct approximation of the steady annual cycle was not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results were in very good agreement. In this way, we achieved a runtime reduction by about 15 \%. The result from the SET algorithm were comparable to those of the full network. Further application of the GA may lead to an even higher reduction.</p> <p>Content:</p> <p>Database sqlite <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN_Database.db">ANN_Database.db</a></p> <p>zip-files with data: </p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Data.zip">ANN-Data.zip</a> structure and weights of used networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Results.zip">ANN-Results.zip</a> results obtained with networks</p> <p><a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/Reference-Results.zip">Reference-Results.zip</a> reference results and training data</p> <p> </p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.