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Local search is one of the simplest families of algorithms in combinatorial optimization, yet it yields strong approximation guarantees for canonical NP-Complete problems such as the traveling salesman problem and vertex cover.
Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.
Furthermore, to approximate solutions to constrained combinatorial optimization problems such as the TSP with time windows, we train hierarchical GPNs (HGPNs) using RL, which learns a hierarchical policy to find an optimal city permutation under constraints.
Ranked #1 on Traveling Salesman Problem on TSPLIB
In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems.
This paper presents a powerful genetic algorithm(GA) to solve the traveling salesman problem (TSP).
We evaluate greedy, 2-opt, and genetic algorithms.
We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution.
We propose a new neural network architecture and use it for the task of statement-by-statement alignment of source code and its compiled object code.
In this paper, we propose a method to solve a bi-objective variant of the well-studied Traveling Thief Problem (TTP).