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, parameterized algorithms, and heuristics.
New ideas in rounding data and dynamic programming, deterministic or
randomized rounding of linear programs, greedy, nature-inspired and
local search algorithms, 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, 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.
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