Combining metaheuristics with mathematical programming, constraint programming and machine learning

During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as...

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Veröffentlicht in:Annals of operations research Jg. 240; H. 1; S. 171 - 215
1. Verfasser: Talbi, El-Ghazali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.05.2016
Springer
Springer Nature B.V
Springer Verlag
Schlagworte:
Algorithms
Artificial intelligence
Business and Management
Combinations (mathematics)
Combinatorics
Communities
Computer Science
Cooperation
Data mining
Heuristic
Heuristic methods
Hybridization
Machine learning
Management research
Mathematical optimization
Mathematical programming
Operations Research
Operations Research/Decision Theory
Optimization
Optimization algorithms
Programming
SI: 4OR Surveys
Studies
Taxonomy
Theory of Computation
Traveling salesman problem
Hybrid metaheuristics
Data mining
Constraint programming
Machine learning
Matheuristics
Mathematical programming
ISSN:0254-5330, 1572-9338
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Abstract During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
AbstractList During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
Issue Title: SURVEYS IN OPERATIONS RESEARCH IV (INVITED SURVEYS FROM "40R," 2012-2014) During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this paper: (1) Combining metaheuristics with complementary metaheuristics. (2) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (3) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (4) Combining metaheuristics with machine learning and data mining techniques.
Audience Academic
Author Talbi, El-Ghazali
Author_xml – sequence: 1
  givenname: El-Ghazali
  surname: Talbi
  fullname: Talbi, El-Ghazali
  email: El-ghazali.Talbi@univ-lille1.fr
  organization: CNRS, INRIA, University of Lille 1
BackLink https://inria.hal.science/hal-01423492$$DView record in HAL
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ContentType Journal Article
Copyright Springer Science+Business Media New York 2015
COPYRIGHT 2016 Springer
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Issue 1
Keywords Hybrid metaheuristics
Data mining
Constraint programming
Machine learning
Matheuristics
Mathematical programming
Language English
License Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
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PublicationTitle Annals of operations research
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Snippet During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for...
Issue Title: SURVEYS IN OPERATIONS RESEARCH IV (INVITED SURVEYS FROM "40R," 2012-2014) During the last years, interest on hybrid metaheuristics has risen...
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SubjectTerms Algorithms
Artificial intelligence
Business and Management
Combinations (mathematics)
Combinatorics
Communities
Computer Science
Cooperation
Data mining
Heuristic
Heuristic methods
Hybridization
Machine learning
Management research
Mathematical optimization
Mathematical programming
Operations Research
Operations Research/Decision Theory
Optimization
Optimization algorithms
Programming
SI: 4OR Surveys
Studies
Taxonomy
Theory of Computation
Traveling salesman problem
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