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The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection [article]

Anja Jankovic, Gorjan Popovski, Tome Eftimov, Carola Doerr
<span title="2021-04-19">2021</span> <span class="release-stage" >pre-print</span>
For those three models -- random forests, decision trees, and bagging decision trees -- the quality of the regression models is highly impacted by the chosen hyper-parameters.  ...  for a more systematic use of classical machine learning models in landscape-aware algorithm selection.  ...  This work has been supported by the Paris Ile-de-France region, and the Slovenian Research Agency (research core funding No. P2-0098, project No. Z2-1867, and grant number PR-10465).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3449639.3459406">doi:10.1145/3449639.3459406</a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.09272v1">arXiv:2104.09272v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rewlb7xqhjakzgquwoxknhrpqi">fatcat:rewlb7xqhjakzgquwoxknhrpqi</a> </span>
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Explainable Landscape-Aware Optimization Performance Prediction [article]

Risto Trajanov and Stefan Dimeski and Martin Popovski and Peter Korošec and Tome Eftimov
<span title="2021-10-22">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Efficient solving of an unseen optimization problem is related to appropriate selection of an optimization algorithm and its hyper-parameters.  ...  In this study, we are investigating explainable landscape-aware regression models where the contribution of each landscape feature to the prediction of the optimization algorithm performance is estimated  ...  We also thank Pascal Kerschke, University of Dresden, for providing us with the ELA feature values for the 24 COCO functions and their 50 instances.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.11633v1">arXiv:2110.11633v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zk7dricidjfibap4syjqosad64">fatcat:zk7dricidjfibap4syjqosad64</a> </span>
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Hyperparameter Ensembles for Robustness and Uncertainty Quantification [article]

Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton
<span title="2021-01-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We further propose a parameter efficient version, hyper-batch ensembles, which builds on the layer structure of batch ensembles and self-tuning networks.  ...  For best performance independent of budget, we propose hyper-deep ensembles, a simple procedure that involves a random search over different hyperparameters, themselves stratified across multiple random  ...  Moreover, we would like to thank Sebastian Nowozin, Klaus-Robert Müller and Balaji Lakshminarayanan for helpful comments on a draft of this paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.13570v3">arXiv:2006.13570v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t47gtxqwebg75moyyzkdbtzp6a">fatcat:t47gtxqwebg75moyyzkdbtzp6a</a> </span>
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An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

Sam Slade, Li Zhang, Yonghong Yu, Chee Peng Lim
<span title="2022-01-30">2022</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a3wauupnbbdj7hbo62upc6grdq" style="color: black;">Neural computing &amp; applications (Print)</a> </i> &nbsp;
Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still  ...  In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.  ...  Code availability The authors will publish the code for the proposed work in a dedicated website after the acceptance of the paper. Declarations  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00521-022-06947-6">doi:10.1007/s00521-022-06947-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tqkjrj2l2vflvcuebl2hcmihsy">fatcat:tqkjrj2l2vflvcuebl2hcmihsy</a> </span>
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Between theory and practice: guidelines for an optimization scheme with genetic algorithms - Part I: single-objective continuous global optimization [article]

Loris Serafino
<span title="2011-12-29">2011</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The rapid advances in the field of optimization methods in many pure and applied science pose the difficulty of keeping track of the developments as well as selecting an appropriate technique that best  ...  The paper can be considered a "collection of tips" (from literature and personal experience) for the non-computer-scientist that has to deal with optimization problems both in the science and engineering  ...  Managing the parameters Evolutionary algorithms are characterized by many parameters which may be used for tuning an algorithm for a specific problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1112.4323v2">arXiv:1112.4323v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/db7274jg45hcfou5onj3yfaipy">fatcat:db7274jg45hcfou5onj3yfaipy</a> </span>
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Algorithm Selection: From Meta-Learning to Hyper-Heuristics [chapter]

Laura Cruz-Reyes, Claudia Gmez-Santilln, Joaqun Prez-Ortega, Vanesa Landero, Marcela Quiroz, Alberto Ocho
<span title="2012-03-02">2012</span> <i title="InTech"> Intelligent Systems </i> &nbsp;
Recent work has focused on creating algorithm portfolios, which contain a selection of state of the art algorithms.  ...  Instead we are likely to attain better results, on average, across many different classes of a problem, if we tailor the selection of an algorithm to the characteristics of the problem instance (Smith-Miles  ...  The adaptive parameter tuning with hyper-heuristics is a recent open research. In order to get a bigger picture of the algorithm performance we need to know them in depth.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5772/36710">doi:10.5772/36710</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7br3tmxzqbggvn3z6v7m63pb3y">fatcat:7br3tmxzqbggvn3z6v7m63pb3y</a> </span>
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Neural Generative Models for Global Optimization with Gradients [article]

Louis Faury and Flavian Vasile and Clément Calauzènes and Olivier Fercoq
<span title="2018-06-14">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In our experiments we show the practical superiority of our algorithm versus classical Evolutionary Search and gradient-based solutions on a benchmark set of multimodal functions, and demonstrate how it  ...  In this paper we focus on the subproblem of global optimization for differentiable functions and we propose an Evolutionary Search-inspired solution where we model point search distributions via Generative  ...  BO has shown state-of-the-art performances for model fitting [1] or hyper-parameter tuning in machine learning [24] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.08594v3">arXiv:1805.08594v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eprurrzrirfyjpdn6w3n3mi3ne">fatcat:eprurrzrirfyjpdn6w3n3mi3ne</a> </span>
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Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

Hannah L. Nicholls, Christopher R. John, David S. Watson, Patricia B. Munroe, Michael R. Barnes, Claudia P. Cabrera
<span title="2020-04-15">2020</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci  ...  ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting  ...  AUTHOR CONTRIBUTIONS HN, PM, MB, and CC outlined and drafted the manuscript. All authors contributed and provided critical review of the manuscript.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2020.00350">doi:10.3389/fgene.2020.00350</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32351543">pmid:32351543</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7174742/">pmcid:PMC7174742</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7h47rgpunvaa5grbz43aswwzey">fatcat:7h47rgpunvaa5grbz43aswwzey</a> </span>
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Zoetrope Genetic Programming for Regression [article]

Aurélie Boisbunon, Carlo Fanara, Ingrid Grenet, Jonathan Daeden, Alexis Vighi, Marc Schoenauer
<span title="2021-02-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
ZGP reaches state-of-the-art performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.  ...  ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms.  ...  To further the comparison for symbolic regression, Figure 3 displays the performance in R2 against the computational time for all SR algorithms (top), and for the top three SR algorithms (bo om), namely  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2102.13388v1">arXiv:2102.13388v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aqnbshovf5e4bchut3neqre57y">fatcat:aqnbshovf5e4bchut3neqre57y</a> </span>
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Predicting effective control parameters for differential evolution using cluster analysis of objective function features

Sean P. Walton, M. Rowan Brown
<span title="2019-06-20">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/recosugx7nevfmgxg6453uyd3a" style="color: black;">Journal of Heuristics</a> </i> &nbsp;
This can have an immediate positive impact on the application of these optimisation algorithms on real world problems, where it is often difficult to select effective control parameters.  ...  We also investigate the key tuning parameters of our methodology, such as number of clusters, which further support the finding that the simple features selected are predictors of effective control parameters  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10732-019-09419-8">doi:10.1007/s10732-019-09419-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lh5eavwr7bcifdejvhzyfjlyuu">fatcat:lh5eavwr7bcifdejvhzyfjlyuu</a> </span>
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A Comparative Analysis of Modeling and Predicting Perceived and Induced Emotions in Sonification

Faranak Abri, Luis Felipe Gutiérrez, Prerit Datta, David R. W. Sears, Akbar Siami Namin, Keith S. Jones
<span title="2021-10-15">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
This research has several applications in automated configurations of hardware devices and their integration with software components in the context of the Internet of Things, for which security is of  ...  This paper describes the development of several machine and deep learning models that predict the perceived and induced emotions associated with certain sounds, and it analyzes and compares the accuracy  ...  Hyper-Parameter Tuning Hyper-parameter tuning is the process of selecting the best parameters for a model to obtain the optimal results.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10202519">doi:10.3390/electronics10202519</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ni37rbvrcbdtjjwhitcaqisuo4">fatcat:ni37rbvrcbdtjjwhitcaqisuo4</a> </span>
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A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [article]

Alejandro Morales-Hernández and Inneke Van Nieuwenhuyse and Sebastian Rojas Gonzalez
<span title="2021-12-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms.  ...  Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems  ...  A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Network Modeling Analysis in Health Informatics and Bioinformatics, 5 (1), 18.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.13755v2">arXiv:2111.13755v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q2qtofihtzev5mose5aj7odfzm">fatcat:q2qtofihtzev5mose5aj7odfzm</a> </span>
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Benchmarking in Optimization: Best Practice and Open Issues [article]

Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore (+5 others)
<span title="2020-12-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient  ...  The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers.  ...  Fonseca for his important input and our fruitful discussion, which helped us shape the section on performance measures. C.  ... 
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Adaptation and Self-Organization in Evolutionary Algorithms [article]

James M Whitacre
<span title="2009-07-03">2009</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This thesis addresses several topics related to adaptation and self-organization in evolving systems with the overall aims of improving the performance of Evolutionary Algorithms (EA), understanding its  ...  relation to natural evolution, and incorporating new mechanisms for mimicking complex biological systems.  ...  Tuan Pham for his guidance, support, and encouragement. His critical eye in the earlier drafts of this thesis helped to greatly strengthen the final version.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/0907.0516v1">arXiv:0907.0516v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/poztzewnofcfljw6bjquysz3d4">fatcat:poztzewnofcfljw6bjquysz3d4</a> </span>
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Privacy-preserving Online AutoML for Domain-Specific Face Detection [article]

Chenqian Yan, Yuge Zhang, Quanlu Zhang, Yaming Yang, Xinyang Jiang, Yuqing Yang, Baoyuan Wang
<span title="2022-03-16">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Despite the impressive progress of general face detection, the tuning of hyper-parameters and architectures is still critical for the performance of a domain-specific face detector.  ...  and (2) how to continuously improve the AutoML algorithm from previous tasks and offer a better warm-up for future ones?  ...  Acknowledgements We thank anonymous reviewers for their valuable feedbacks. We also thank Chengmin Chi (STCA) and Xiaotian Gao (MSRA) for their suggestions.  ... 
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