We use heuristics to solve computationally difficult problems where optimal solutions are too expensive to deploy, hard to manage, or otherwise inefficient. Our prior work, MetaOpt, shows many of the ...
Identified and explained in detail the gaps and possible future works for improvement in two popular research papers that used heuristic and meta-heuristic algorithms to solve multi-objective vehicle ...
Abstract: Driven by an unprecedented surge in freight transportation and city logistics, this paper tackles a practical variant of the famous Vehicle Routing Problem that jointly accounts for the ...
Active learning enables prediction models to achieve better performance faster by adaptively querying an oracle for the labels of data points. Sometimes the oracle is a human, for example when a ...
A key question about LLMs is whether they solve reasoning tasks by learning transferable algorithms or simply memorizing training data. This distinction matters: while memorization might handle ...
Abstract: Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such ...
Adam Satariano and Roser Toll Pifarre interviewed more than 50 victims, families, police, government officials and other experts about Spain’s gender violence program. In a small apartment outside ...
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