Models for iterative global optimization
Models for iterative global optimization
Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
Learning evaluation functions for global optimization and Boolean satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Approximation algorithms
Neuro-Dynamic Programming
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Reinforcement learning for job shop scheduling
Reinforcement learning for job shop scheduling
Learning evaluation functions for global optimization
Learning evaluation functions for global optimization
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Previous researches have shown the success of using Reinforcement Learning in solving combinatorial optimization problems. The main idea of these methods is to learn (near) optimal evaluation functions to improve local searches and find (near) optimal solutions. STAGE algorithm, introduced by Boyan & Moore, is one of the most important algorithms in this area. In this paper, we focus on Bin-Packing problem, an important NP-Complete problem. We analyze cost surface structure of this problem and investigate "big valley" structure for the set of its local minima. The result gives reasons for STAGE's success in solving this problem. Then by comparing the results of experiments on Bin-Packing problem, we analyze the effectiveness of steepest-descent hill climbing, stochastic hill climbing and first-improvement hill climbing as the local search algorithms in STAGE.