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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 931940 of 1277 papers

TitleStatusHype
A Knowledge Representation Approach to Automated Mathematical Modelling0
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning0
Algorithms Inspired by Nature: A Survey0
Algoritmos Genéticos Aplicado ao Problema de Roteamento de Veículos0
A Local Optima Network View of Real Function Fitness Landscapes0
A machine learning framework for neighbor generation in metaheuristic search0
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem0
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise0
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems0
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