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Dataset results
11 results for “optimism bias”
Experimental Data for the Paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient"
<p><strong><em>The data for the paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient"</em></strong></p> <p>This is the data set with the experimental results for our paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient." We conduct more than 56 million runs, consuming more than 6.5*10<sup>14</sup> FEs as well as 150 processor years, ensuring that our results are statistically sound and rigorous. Here, you can find all the results, all the program codes used for obtaining the results, and all the tables and figures produced from the results, and the program codes used to produce them.</p> <p><strong><em>Included Files</em></strong></p> <ul> <li><code>ffa-empirical-complexity_results.tar.xz</code> (size packed 11.7 GiB, unpacked 335.2 GiB): The complete set of log files. For each run of each experiment, one distinct text-based log file is created. The log file contains every improving step of the algorithm, the final result, and the system configuration. This archive is very large and unpacked it will occupy more than 335 GiB of hard disk space.</li> <li><code>ffa-empirical-complexity_end_of_run_results_and_stats.tar.xz</code> (size packed 861.4 MiB, unpacked 6,295.9 MiB): The end-of-run result qualities and consumed runtime as well as statistics thereof. These information have been extracted from the log files and are provided in form of semicolon-separated values text files. These files are much easier to consume. They do not reflect the progress of the single runs, but only their end results.</li> <li><code>ffa-empirical-complexity_sources.tar.xz</code> (size packed 65.5 MiB, unpacked 173.6 MiB): The complete set of <code>Java</code> sources that was used to perform the experiments. Since the random seeds of the random number generators are created in a deterministic way, you could execute this code and obtain the exactly same log files in terms of consumed FEs, improving steps, and end results as we provide in <code>ffa-empirical-complexity_results.tar.xz</code>. (Of course, your systems configuration and measured runtime in milliseconds would probably be different.)</li> <li><code>ffa-empirical-complexity_evaluator.tar.xz</code> (size packed 4,015.8 KiB, unpacked 4,510 KiB): The <code>Java</code> and <code>R</code> source codes that are used to extract the end results from the log files, compute all relevant statistics, and produce the graphics in our article.</li> <li><code>ffa-empirical-complexity_evaluation.tar.xz</code> (size packed 417.3 KiB, unpacked 590.0 KiB): The high-level conclusions produced by the evaluator from the raw data, including tables and figures.</li> <li><code>ffa-empirical-complexity_saga_ffa_on_plateau.tar.xz</code> (size packed 7,238.9 KiB, unpacked 26.8 MiB): We also conducted an additional experiment to better understand the behavior of the SAGA algorithm variants using FFA on the Plateau problem. Here we provide source codes and result log files of this experiment. The result log files are much more comprehensive, as we tried to figure out why these algortihms were able to solve the Plateau problems (and they ultimately helped us to successfully do so).</li> </ul> <p><strong><em>License</em></strong></p> <p>The copyright holder of this dataset is Prof. Dr. Thomas Weise (see <a href="#contact">Contact</a>). The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/en/legalcode">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong><em>Contact</em></strong></p> <p>If you have any questions or suggestions, please contact the corresponding author of this dataset, Prof. Dr. <a href="http://iao.hfuu.edu.cn/team/director">Thomas Weise</a> of the Institute of Applied Optimization (<a href="http://iao.hfuu.edu.cn/">IAO</a>) at <a href="http://www.hfuu.edu.cn/english/main.htm">Hefei University</a> [<a href="http://www.hfuu.edu.cn">合肥学院</a>] in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
Optimizing Attentional Bias Modification
ClinicalTrials.gov study NCT05816564. IPD Sharing: NO. Countries: 1. Publications: 13.
Implementation and Results of a "Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem"
<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) with and without FFA applied to the Quadratic Assignment Problem. These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Jiayang Chen, Zhize Wu, Sarah L. Thomson, and Thomas Weise. Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. In <em>16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24)</em>. November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Jiayang CHEN (陈嘉阳) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:cjy65820607@163.com">cjy65820607@163.com</a>.</p>
Feasibility Study Using Run-to-Run Control to Optimize Continuous Glucose Sensor Bias
ClinicalTrials.gov study NCT02290600. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Current nest box designs may not be optimal for the larger forest dormice; pre-hibernation increase in body mass might lead to sampling bias in ecological data
<p>Biologists commonly use nest boxes to study small arboreal mammals, including forest dormouse (Dryomys nitedula). Hibernating dormouse species often experience pronounced seasonal variations in body mass, which might lead to sampling biases if it is not taken into account when designing nest boxes. In our study of forest dormouse, we noticed that the entrance hole of nest boxes had been gnawed on. We hypothesized that this behavior was exhibited by individual dormice who had higher body mass and, therefore, were unable to pass through the entrance holes. To test our hypothesis, we categorized individual dormice present inside nest boxes based on their body mass; then compared the seasonal body mass dynamics with the timing of the gnawing behavior. We also compared nest box occupancy by forest dormouse before and after the gnawing behavior. Interestingly, we found that the gnawing behavior was displayed exclusively when part of the dormouse population increased considerably in body mass, which supports our hypothesis. Additionally, nest box occupancy decreased significantly from 20% before to 4.6% after the gnawing behavior. We suggest that researchers use nest boxes with entrance holes larger than 4 cm in future studies of forest dormouse to prevent the possible exclusion of the conspecifics that have higher body mass before hibernation. This type of sampling bias can probably happen in studies of other species, such as fat dormouse, that similarly show pronounced seasonal variations in body mass. We recommend that biologists consider the seasonal body mass dynamics of the target species when designing nest boxes to minimize bias in ecological data and improve management actions.</p>
Current nest box designs may not be optimal for the larger forest dormice; pre-hibernation increase in body mass might lead to sampling bias in ecological data
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Digital RNA Sequencing Minimizes Sequence-Dependent Bias and Amplification Noise with Optimized Single Molecule Barcodes
GEO Series GSE34449. Escherichia coli str. K-12 substr. MG1655. 2 samples. Type: Expression profiling by high throughput sequencing.
Sentiment analysis of media's political bias in micro-blogging - A computational framework for optimized recommendation systems
<p>This dataset contains tweets from four Pakistani news channels, namely DAWN, GEO, ARY, and 24News, collected using the twarc command line tool with a Twitter academic researcher account. The tweets were collected between Nov, 2015, and April 4, 2022, and relate to three major political parties in Pakistan, PTI, PMLN, and PPP through their official names in the relevant news channel page tweets. </p>
Optimizing OR Simulation in MEDI 045: Bias Reduction and Strategic Improvements (OPTIMIST)
ClinicalTrials.gov study NCT06753266. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Transcript profiling of common bean using the Soybean Genome Array: optimizing analysis by masking biased probes
GEO Series GSE18822. Glycine max; Phaseolus vulgaris. 18 samples. Type: Expression profiling by array.
Clinical Study of G Protein Biased Μ - Opioid Receptor Agonist Oliceridine for Optimizing Postoperative Analgesia Under General Anesthesia
ClinicalTrials.gov study NCT06668298. IPD Sharing: NO. Countries: 0. Publications: 0.
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