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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 431–440 of 1277 papers

TitleStatusHype
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps—0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem—0
Assessing Distribution Network Flexibility via Reliability-based P-Q Area Segmentation—0
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem—0
Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression—0
Estudo comparativo de meta-heurísticas para problemas de colorações de grafos—0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
Deep Generative Model for Mechanical System Configuration Design—0
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