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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 951–960 of 1277 papers

TitleStatusHype
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing—0
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules—0
An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems—0
A new dog learns old tricks: RL finds classic optimization algorithms—0
A new hybrid genetic algorithm for protein structure prediction on the 2D triangular lattice—0
A new neighborhood structure for job shop scheduling problems—0
An Expandable Machine Learning-Optimization Framework to Sequential Decision-Making—0
An Improved ACS Algorithm for the Solutions of Larger TSP Problems—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
An interacting replica approach applied to the traveling salesman problem—0
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