Assessing the Justification for Integrating Deep Learning in Combinatorial Optimizationby@heuristicsearch
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Assessing the Justification for Integrating Deep Learning in Combinatorial Optimization

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This summary highlights the importance of conducting thorough comparisons between deep learning-integrated heuristics and classical heuristics in combinatorial optimization. It emphasizes the need to articulate both strengths and weaknesses to guide research investments and justify the integration of deep learning architectures.
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(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;

(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University.

Abstract & Introduction

Related work

Evaluation for Max-Cut

Evaluation for SAT

Summary and Outlook, References

Supplementary Materials


Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics.


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