Hi everyone, I am very pleased to announce the broadening of the Heuristic Search area of
INFORMS Journal on Computing to now include Approximation Algorithms. The full description is below and more information can be found on the journal's website :
https://pubsonline.informs.org/journal/ijocAlice Smith, Editor in Chief
Heuristic Search and Approximation Algorithms
This area focuses on the design, learning, and application of efficient and innovative methods for approximately solving relevant, difficult (combinatorial) optimization problems. In particular, it covers topics such as approximation algorithms with performance guarantee, (fully) polynomial time approximation schemes, greedy and local search algorithms, metaheuristics, and neural network techniques. New ideas in rounding data and dynamic programming, deterministic or randomized rounding of linear programs, greedy and local search algorithms, metaheuristics, nature inspired methods, neural networks, and constrained programming-based learning, or hybrid approaches combining existing heuristic methods, alone or in conjunction with techniques from other areas of operations research or computer science, e.g. neural networks and constraint programming, are also of particular interest.
The emphasis within the area is on papers presenting methodological innovations that can be applied to a wide range of problems or situations and a proof of performance. Papers must clearly elaborate the innovation either in modeling, or a new solution approach, justified theoretically or via computational comparison with existing methods or in a real application. A simple assertion that the innovations can be applied elsewhere does not meet the burden of proof required in good scientific practice. The authors must demonstrate generality in a convincing manner, either experimentally or theoretically. Rarely, it may be clear that a method is broadly important even when it is tested on only one problem, but this would be very unusual.
For many outlets in our field, a necessary and sufficient condition for publication is to show better results than a set of competitors over some test instances. While this may be a reasonable standard in some settings, it is generally insufficient for the INFORMS Journal on Computing, especially given that a significant fraction of our audience are not specialists in heuristic search and learning. Obtaining best-known results will be helpful in making the case for the paper, but it is not normally sufficient.
The problems considered in the paper must be "important" in some sense, though this is difficult to define precisely and is subject to some tradeoffs. Importance may be demonstrated by application to a problem of practical significance, or demonstration that the problem has been extensively studied in the research literature, for example.
A scientifically rigorous paper presenting methodological innovations that can be applied to a wide range of important problems or situations is not easy to produce. However, it is relatively easy to recognize such a paper. Papers in this category will not generally require multiple major revisions: a paper presenting methodological innovations that can be applied to a wide range of problems or situations does not need a committee to make it perfect. Conversely, the referees generally cannot walk authors through the process of discovering and describing methodological innovations that can be applied to a wide range of problems or situation.
Survey papers covering recent advances in a given field and papers aimed at providing a conceptual integration of the area are also welcome.
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Alice Smith
INFORMS Journal on Computing, Editor in Chief
Forehand/Accenture Distinguished Professor
Auburn University
Auburn AL
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