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A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

Xinyi Yang, Ziyi Wang, Hengxi Zhang, Nan Ma, Ning Yang, Hualin Liu, Haifeng Zhang, Lei Yang
2022 Algorithms  
Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems.  ...  Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance.  ...  Therefore, strong machine learning algorithms are proposed to solve problems with those two structures and can be applied in COPs conveniently.  ... 
doi:10.3390/a15060205 fatcat:tea63hytzrau7jitrwwapl3bfi

Simulated annealing and boltzmann machines: A stochastic approach to combinatorial optimization and neural computing

1990 Discrete Applied Mathematics  
Learning in practice (Choosing a desired visible behaviour. Convergence properties. Estimation of the activation pro- babilities. Termination of the learning algorithm).  ...  Emile Aarts and Jan Korst, Simulated Annealing and Boltzmann Machines: A I: SIMULATED ANNEALING. 1: Combinatorial Optimization. Combinatorial Optimization and Boltzmann Machines. General strategy.  ... 
doi:10.1016/0166-218x(90)90039-f fatcat:qey3ucdtx5gptcprwpporwqa3m

Field theory: Classical foundations and multiplicative groups

1990 Discrete Applied Mathematics  
Learning without hidden units (Outline of the learning algorithm. Estimation of activation probabilities). Learning with hidden units. Variants of the learning algorithm.  ...  Learning in practice (Choosing a desired visible behaviour. Convergence properties. Estimation of the activation pro- babilities. Termination of the learning algorithm).  ... 
doi:10.1016/0166-218x(90)90037-d fatcat:fwhb3aal7zes3n5bu7ios2nv4e

A course in number theory and cryptography

1990 Discrete Applied Mathematics  
Learning in practice (Choosing a desired visible behaviour. Convergence properties. Estimation of the activation pro- babilities. Termination of the learning algorithm).  ...  Emile Aarts and Jan Korst, Simulated Annealing and Boltzmann Machines: A I: SIMULATED ANNEALING. 1: Combinatorial Optimization. Combinatorial Optimization and Boltzmann Machines. General strategy.  ... 
doi:10.1016/0166-218x(90)90038-e fatcat:gvpbko7virdenao6qzj6xapo3y

Machine learning and combinatorial optimization, editorial

Gianni A. Di Caro, Vittorio Maniezzo, Roberto Montemanni, Matteo Salani
2021 OR spectrum  
Introduction Machine learning has recently emerged as a prospective area of investigation for OR in general and specifically for combinatorial optimization.  ...  combinatorial optimization problems using a blending of different machine learning techniques.  ... 
doi:10.1007/s00291-021-00642-z fatcat:3rpebuib4bbhffzdwricqi2dcy

Guest editorial: special issue on automated design and adaptation of heuristics for scheduling and combinatorial optimisation

Su Nguyen, Yi Mei, Mengjie Zhang
2017 Genetic Programming and Evolvable Machines  
This special issue aims to present the most recent advances in scheduling and combinatorial optimisation with a special focus on two themes: (1) automated heuristic design, and (2) self-adaptive algorithms  ...  Scheduling and combinatorial optimisation problems appear in many practical applications in production and service industries and have been the research interest of researchers from operations research  ...  In ''Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment'', the authors investigate different ways to take advantages of ensemble  ... 
doi:10.1007/s10710-017-9317-9 fatcat:6hprk2kuhvcwre4onxungvvbeu

Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers [article]

Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, Andrea Lodi
2020 arXiv   pre-print
We present Ecole, a new library to simplify machine learning research for combinatorial optimization.  ...  Ecole exposes several key decision tasks arising in general-purpose combinatorial optimization solvers as control problems over Markov decision processes.  ...  Acknowledgements This work was supported by the Canada Excellence Research Chair (CERC) in Data Science for Real-Time Decision Making and IVADO.  ... 
arXiv:2011.06069v2 fatcat:pghkxw2z55ekvnkepcmogsiq2e

Smoke Testing for Machine Learning: Simple Tests to Discover Severe Defects [article]

Steffen Herbold, Tobias Haar
2021 arXiv   pre-print
severe bugs, even in mature machine learning libraries.  ...  Even though our approach is almost trivial, we were able to find bugs in all three machine learning libraries that we tested and severe bugs in two of the three libraries.  ...  In Section 2, we discuss the related work on testing machine learning algorithms. Afterwards, we present our approaches for smoke testing in Section 3 and combinatorial smoke testing in Section 4.  ... 
arXiv:2009.01521v2 fatcat:cxkwgahwhjb47g7lu3hjveuwj4

Constrained Machine Learning: The Bagel Framework [article]

Guillaume Perez, Sebastian Ament, Carla Gomes, Arnaud Lallouet
2021 arXiv   pre-print
The goal of this paper is to broaden the modeling capacity of constrained machine learning problems by incorporating existing work from combinatorial optimization.  ...  Constrained machine learning problems are problems where learned models have to both be accurate and respect constraints.  ...  Recently, the use of combinatorial optimization solver for machine learning has been done in the context of target moving [10] .  ... 
arXiv:2112.01088v1 fatcat:ewvq6r6i6nazhmhgpqpb6qz7qq

Machine Learning for Portfolio Selection Using Structure at the Instance Level [chapter]

Cormac Gebruers, Alessio Guerri
2004 Lecture Notes in Computer Science  
We apply the Machine Learning methodologies using the extracted features as input data and the best algorithms as prediction classes. Table 1 shows the prediction rates we obtain.  ...  Our purpose is twofold: firstly, to show that structure at the instance level is tightly connected to algorithm performance, and secondly to demonstrate that different machine learning and modelling methodologies  ...  We apply the Machine Learning methodologies using the extracted features as input data and the best algorithms as prediction classes. Table 1 shows the prediction rates we obtain.  ... 
doi:10.1007/978-3-540-30201-8_73 fatcat:sdppyxuvbrc4rczmsypvulvtny

Learning in brain and machine—complexity, Gödel, Aristotle

Leonid Perlovsky
2013 Frontiers in Neurorobotics  
As discussed above, machine learning and mathematical models of the mind face algorithmic difficulties related to combinatorial complexity.  ...  To make machines capable of learning and to model mathematically the learning abilities of the mind, new types of algorithms are needed that avoid combinatorial complexity.  ... 
doi:10.3389/fnbot.2013.00023 pmid:24348376 pmcid:PMC3842154 fatcat:5dkntejtkzf67pogqvd73v7oby

Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization

Wenfa Ng, Auctores Publishing LLC
2021 Biotechnology and Bioprocessing  
machine learning tools.  ...  modern machine learning tools.  ...  This is made even harder by the cryptic nature of machine learning algorithms.  ... 
doi:10.31579/2766-2314/060 fatcat:cdwgfaipjbdxln6mar7fvjndoy

Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking

Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, Magnus Boman
2020 IEEE Access  
Relevant developments in machine learning research on graphs are surveyed for this purpose.  ...  Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances.  ...  Junaid Shuja, who coordinated the review process, and to the anonymous reviewers for their expeditious and helpful review comments received in preparation of the final version of the manuscript.  ... 
doi:10.1109/access.2020.3004964 fatcat:v7i7x6p77zfi7dntipxoiolily

Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking [article]

Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, Magnus Boman
2020 arXiv   pre-print
Relevant developments in machine learning research on graphs is surveyed, for this purpose.  ...  Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances.  ...  have prompted machine learning interest in tackling combinatorial tasks (Section II-B). • Outline contemporary machine learning concepts and methods employed for solving combinatorial optimization problems  ... 
arXiv:2005.11081v1 fatcat:ajqghcevqvdrvdlcrknxlzlqdi

A Survey on Reinforcement Learning for Combinatorial Optimization [article]

Yunhao Yang, Andrew Whinston
2020 arXiv   pre-print
learning algorithms in recent years.  ...  This paper gives a detailed review of reinforcement learning in combinatorial optimization, introduces the history of combinatorial optimization starting in the 1960s, and compares it with the reinforcement  ...  This trend may be significant when quantum computing is widely used in machine learning.  ... 
arXiv:2008.12248v2 fatcat:e6zulkwqajfabislo24zjvjsy4
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