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Experimental Algorithmics (JEA)

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JEA Guidelines for Authors

The ACM JEA is a high-quality, refereed, archival journal devoted to the study of discrete algorithms and data structures through a combination of experimentation and classical analysis and design techniques.  ACM JEA is an entirely online journal.

Original submissions are sought that address implementation and performance issues of discrete algorithms and data structures. An experimental study typically includes an implementation, a series of experiments designed to understand the behavior of the algorithm(s) under study, and a critical discussion of the experiments and their results. Whenever possible, experiments should include test data from previously published studies to enable critical comparisons, although the development of new test suites is also encouraged. Studies of an algorithm in a specific application context of general interest are welcome, as are contributions in the development and understanding of experimental methodologies, including multimedia tools such as algorithm animation.

Also within the scope of the ACM JEA are research contributions in the area of test generation and result assessment as applied to discrete algorithms and data structures. Fundamental and application areas include, but are not limited to: combinatorial optimization, computational biology, computational geometry, graph manipulation, graphics, heuristics, network design, parallel processing, routing, searching and sorting, scheduling, and VLSI design.

Submissions to JEA typically include an article, a suite of programs, and a collection of test data and computational results. Accepted submissions are placed on-line, with code and data made available for use by researchers and practitioners alike.

JEA joins RCR: Replicated Computational Results Initiative

Authors of articles nearly-accepted in JEA will now be invited to apply for an RCR certificate attached to their article. For those that accept, a further reviewer will be appointed to ensure that their experimental results can be replicated. The RCR initiative aims to improve the reproducibility of experimental results in the community and adds to the trustability of the experimental results in the articles published in JEA. For more details see RCR Initiative page.

 
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