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Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein,Carola Doerr,Jakob Bossek,Sowmya Chandrasekaran,Tome Eftimov,Andreas Fischbach,Pascal Kerschke,Manuel López-Ibáñez,Katherine M. Malan,Jason H. Moore,Boris Naujoks,Patryk Orzechowski,Vanessa Volz,Markus Wagner,Thomas Weise +14 more
TL;DR: The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility.
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Abstract: This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice in benchmarking is its main goal. The article discusses eight essential topics in benchmarking: clearly stated goals, well- specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility. The final goal is to provide well-accepted guidelines (rules) that might be useful for authors and reviewers. As benchmarking in optimization is an active and evolving field of research this manuscript is meant to co-evolve over time by means of periodic updates.
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Citations
•Journal Article
The Design and Analysis of Experiments
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
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Modified distance calculation in generational distance and inverted generational distance
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TL;DR: It is demonstrated using simple examples that some Pareto non-compliant results of GD and IGD are resolved by the modified distance calculation and it is shown that IGD with themodified distance calculation is weakly PareTO compliant whereas the original IGD is Pare to non- Compliant.
288
Metaheuristics “In the Large”
Jerry Swan,Steven Adriaensen,Alexander E. I. Brownlee,Kevin Hammond,Colin G. Johnson,Ahmed Kheiri,Faustyna Krawiec,J. J. Merelo,Leandro L. Minku,Ender Özcan,Gisele L. Pappa,Pablo García-Sánchez,Kenneth Sörensen,Stefan Voß,Markus Wagner,David White +15 more
TL;DR: The metaheuristics "In the Large" project as discussed by the authors aims to support the development, analysis, and comparison of new approaches in optimization research by providing extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems.
57
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TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.