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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

Showing 11811190 of 1277 papers

TitleStatusHype
ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps0
Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression0
Estudo comparativo de meta-heurísticas para problemas de colorações de grafos0
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization0
Evaluation of bioinspired algorithms for the solution of the job scheduling problem0
Evolutionary Approach for the Containers Bin-Packing Problem0
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives0
Evolutionary Construction of Perfectly Balanced Boolean Functions0
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits0
Evolutionary RL for Container Loading0
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