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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 441450 of 1277 papers

TitleStatusHype
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
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise0
Fast Hyperparameter Tuning for Ising Machines0
Exact and Approximate Hierarchical Clustering Using A*0
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem0
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