Aims and Scope
The ACM JEA has been established to address the following issues:
- The empirical study of combinatorial algorithms is a rapidly growing
research area, with no proper outlet for publication.
- Communication among researchers in this area must include more than
a summary of results or a discussion of methods; the actual programs
and data used are of critical importance.
- Many of the algorithms and data structures published over the last
ten years have never been implemented by anyone and are at risk of remaining
theoretical ``curiosities.'' To bring such algorithms and data structures
into the practical realm often requires considerable sophistication;
researchers need to be encouraged to turn their talents in that direction.
- Most researchers find that they must program their own version of this
or that well-known algorithm or data structure, because repositories
for these are not available.
- The two preceding reasons also explain why practitioners only rarely use
state-of-the-art algorithms and data structures;
a repository of routines, most with well documented behavior on realistic
test cases, will encourage practitioners to use more recent results.
Original submissions are sought that address implementation and performance
issues of discrete algorithms and data structures. An experimental study
includes an implementation, a series of experiments designed to ascertain
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
animations.
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, computational learning theory, graph manipulation, graphics, heuristics,
network design, parallel processing, routing, searching and sorting, scheduling,
and VLSI design.
