Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization. However, these algorithms may require careful hyperparameter tuning for each problem instance. We use a reinforc...
This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In...
Accurate automatic optimization heuristics are necessary for dealing with the complexity and diversity of modern hardware and software. Machine learning is a proven technique for learning such heuristics, but its success is bound by the quality of the features used. These features must be hand crafted...
Optimization of Heterogeneous Systems with AI Planning Heuristics and Machine Learning: A Performance and Energy Aware Approach We consider a recently introduced class of network construction problems where edges of a transportation network need to be constructed by a server (constr... S Memeti,S Pll...
One alternative is to pre-select a small set of "best" location candidates prior to the application of the optimal facility location models and use these points as input to the optimization model. The choice of these candidates is crucial to the quality of the optimal solution. Meanwhile, the...
These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic. 展开 关键词: Hybrid algorithms Combinatorial optimization Metaheuristics Machine ...
You need to elaborate on company priorities and capabilities to decide on the approach and tools you should choose. If heuristic and optimization algorithms are compared in terms of solution quality, the latter is the clear winner. Solution quality is often a critical success factor for ta...
Summary: Many problems in combinatorial optimization are NP-hard. This has forced researchers to explore meta-heuristic techniques for dealing with this class of complex problems and finding an acceptable solution in reasonable time. The... D Boughaci,H Drias 被引量: 30发表: 2005年 Iterative sem...
Meta-heuristic approaches to select important features only for performance enhancement in effective Intrusion Detection System. machine-learning optimization feature-selection naive-bayes-classifier intrusion-detection differential-evolution particle-swarm-optimization knn-classification meta-heuristics flower-pollinati...
reinforcement-learning genetic-algorithm evolutionary-algorithms vehicle-routing-problem ant-colony-optimization neural-combinatorial-optimization traveling-salesman-problem hyper-heuristics orienteering-problem multiple-knapsack-problem bin-packing-problem large-language-models automatic-algorithm-generation llm-agent ...