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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
A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution0
Evolutionary RL for Container Loading0
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits0
Chemical Reaction Optimization for the Set Covering Problem0
Evolutionary Construction of Perfectly Balanced Boolean Functions0
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives0
Cheaper and Better: Selecting Good Workers for Crowdsourcing0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms0
Annealed Training for Combinatorial Optimization on Graphs0
Evolutionary Approach for the Containers Bin-Packing Problem0
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