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Everything about Empirical algorithmics
Use of empirical methods to study algorithms
In computer science, empirical algorithmics is the practice of using empirical methods to study the behavior of algorithms. The practice combines algorithm development and experimentation: algorithms are not just designed, but also implemented and tested in a variety of situations. In this process, an initial design of an algorithm is analyzed so that the algorithm may be developed in a stepwise manner. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Empirical algorithmicsA 5-minute orientation
- Skim a recent paper: Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literatureInfluential work since 2022
- Pick up a classic book: Some foundations for empirical study in the Euclidean spatial model of social choiceThe most-reprinted book on the subject
- Read the landmark paper: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy functionCited 9,148 times
- Go sideways: Algorithm engineeringA closely related topic
- Go sideways: Profiling (computer programming)A closely related topic
đź“– Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
🆕 Influential since 2022
Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study
Review and empirical analysis of sparrow search algorithm
đź“š Books worth reading
Some foundations for empirical study in the Euclidean spatial model of social choiceCraig A. Tovey · 1991
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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