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15 results for “evolutionary algorithm”
Data and code for the paper "Precision Groundwater Modeling: when cokriging meets evolutionary and iterative algorithms"
<ul> <li>exemplary dataset for 2019 yearly water table measurements in Northeaster Italy</li> <li>MATLAB code for the pre-processing GA-driven and the post-processing iterative validation part</li> </ul>
Datasets to accompany "Evolutionary Dataset Optimisation: learning algorithm quality through evolution"
<p>This archive contains the datasets generated to accompany the work entitled "Evolutionary Dataset Optimisation: learning algorithm quality through evolution". The source code used to generate these datasets is archived <a href="https://doi.org/10.5281/zenodo.3492236">here</a>.</p> <p>Details on how to use this archive are given in the README.</p>
Artifact for "Using Evolutionary Algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays"
<p>This artifact accompanies the paper "Using Evolutionary Algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays", accepted to the International Conference on Performance Engineering (ICPE) 2024.</p> <p>Please see <code>README.md</code> for more information.</p>
Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms - Dataset
<p>This repository contains the raw data and code nessecary to reproduce the results from the paper "Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms"</p> <p>The main file is the python-notebook 'reproducibility.ipynb', which details the full process for reproduction of the results shown in the paper. The two additional .py files are included for computation which takes longer and can be parallelized.</p> <p>The folder 'irace_conf_static_modcma.zip' contains the verification runs: 200 independent runs of each configuration. Indexes are according to 'Irace_confs_static_modcma_v2.csv'</p> <p>The folder 'logs_baseline_cs.zip' contains the raw irace files on which the analysis is based. This data is taken from the following repository:<br> de Nobel, Jacob, Vermetten, Diederick, Wang, Hao, Doerr, Carola, & Bäck, Thomas. (2021). Data and Code from: Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4524959</p>
Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm
<p>Dataset and code for the variants of the traveling salesman problem with time windows using multifactorial evolutionary algorithm</p>
Data for "Heuristic algorithms in Evolutionary Computations and modular organization of biological macromolecules: applications to in vitro evolution"
<p>This publication contains data for the construction of the figures and tables for the paper "Heuristic algorithms in Evolutionary Computations and modular organization of biological macromolecules: applications to in vitro evolution" accepted to publication in PLOS ONE.</p>
Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems (Code and Dataset)
<p>This is the code and the dataset for our paper published at IEEE WCCI/CEC 2024, <a href="https://2024.ieeewcci.org/">https://2024.ieeewcci.org</a>.</p> <p>Title: "Towards Adaptation in Multiobjective Evolutionary Algorithms for Integer Problems"<br>Abstract: Parameter control refers to the techniques that dynamically adapt the parameter values of the evolutionary algorithm during the optimization process, such as population size, crossover rate, or operator selection. Adaptation can improve the performance and robustness of the algorithm, however, parameter control mechanisms themselves need to be designed and configured carefully. With this article, we contribute a systematic investigation of an adaptive, multi-objective algorithm that is designed for the optimisation of integer decision spaces. We find that (1) adaptation outperforms the best static configurations, and (2) performance of the multi-objective algorithm is often independent of the adaptation scheme's initial configuration.</p>
Bug Report Analytics for Software Reliability Assessment using Hybrid Swarm-Evolutionary Algorithm
<p><span>There are in total 6 files.</span></p> <p><span><span>1.<span> </span></span></span><span>Out of these files three documents are related to datasets. Two are related to unrefined Eclipse and JDT files and third is refined data of Eclipse and JDT Project Failure Datasets which has been used for experimentation purpose.</span></p> <p><span><span>2.<span> </span></span></span><span>This package also includes code for all the models version wise for all versions of Eclipse and JDT projects.</span></p> <p><span><span>3.<span> </span></span></span><span>Sample Code has also been given for version 4.3 and 4.10. </span></p> <p><span>Steps to run </span></p> <p><span><span>a)<span> </span></span></span><span>In this code, Main ABCDE file needs to be run and different datasets could be executed on this file. This is for one type of datasets that is time domain dataset only. </span></p> <p><span><span>b)<span> </span></span></span><span>If anyone is interested in getting separate results for cumulative sum and failure intensity, separate file has been given. </span></p> <p><span><span>c)<span> </span></span></span><span>Code for ABCDE algorithm that is Swarm Evolutionary algorithm used in the paper has also been given in these files.</span></p>
Data Sets for the study "What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms"
<p>This is the dataset of the paper "What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms"</p> <ul> <li>figures.ipynb provides postprocessing codes for using the attached data to generate tables and figures in the paper.</li> <li>the csv folder consists of the raw data of function evaluations that algorithms used to hit each solution the first time.</li> <li>the metric folder consists of the processed data, which records the convergence process of two single objectives (y1 and y2), Hypervolume, and the number of obtained Pareto solutions.</li> <li>the gif folder consists of the gif files plotting the convergence process of solving LOTZ.</li> </ul> <p> </p> <p>The code used to generate this data is available at https://github.com/FurongYe/GSEMO</p>
Optimization of Cochlear Implant Fitting in Patients With Functional Contralateral Hearing Using an Evolutionary Algorithm.
ClinicalTrials.gov study NCT04888143. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Improving HybrID: how to best combine indirect and direct encoding in evolutionary algorithms
Many challenging engineering problems are regular, meaning solutions to one part of a problem can be reused to solve other parts. Evolutionary algorithms with indirect encoding perform better on regular problems because they reuse genomic information to create regular phenotypes. However, on problems that are mostly regular, but contain some irregularities, which describes most real-world problems, indirect encodings struggle to handle the irregularities, hurting performance. Direct encodings are better at producing irregular phenotypes, but cannot exploit regularity. An algorithm called HybrID combines the best of both: it first evolves with indirect encoding to exploit problem regularity, then switches to direct encoding to handle problem irregularity. While HybrID has been shown to outperform both indirect and direct encoding, its initial implementation required the manual specification of when to switch from indirect to direct encoding. In this paper, we test two new methods to improve HybrID by eliminating the need to manually specify this parameter. Auto-Switch-HybrID automatically switches from indirect to direct encoding when fitness stagnates. Offset-HybrID simultaneously evolves an indirect encoding with directly encoded offsets, eliminating the need to switch. We compare the original HybrID to these alternatives on three different problems with adjustable regularity. The results show that both Auto-Switch-HybrID and Offset-HybrID outperform the original HybrID on different types of problems, and thus offer more tools for researchers to solve challenging problems. The Offset-HybrID algorithm is particularly interesting because it suggests a path forward for automatically and simultaneously combining the best traits of indirect and direct encoding.
Data from: A general and efficient algorithm for the likelihood of diversification and discrete-trait evolutionary models
Open the record for dataset details and reuse information.
Data from: Improving HybrID: how to best combine indirect and direct encoding in evolutionary algorithms
Open the record for dataset details and reuse information.
Solving the multi-commodity flow problem using an evolutionary routing algorithm in a computer network environment
<p>The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that takes into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Networks (SDN) controller. The use of such information ensures that no link is used beyond its capacity, eliminating network congestion. We use EAs as true multi-objective solvers to provide a set of valid routing solutions from a single run of the algorithm. Modifications to the Multipath TCP (MPTCP) protocol are proposed to overcome the multipath problems associated with TCP.</p>
Comparison of Speech Understanding Between Tonotopy-based Fitting and Setting Based on Evolutionary Algorithms
ClinicalTrials.gov study NCT06737185. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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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.