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Combining metaheuristics with mathematical programming, constraint programming and machine learning

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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: (i) Combining metaheuristics with complementary metaheuristics. (ii) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (iii) Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community. (iv) Combining metaheuristics with machine learning and data mining techniques.

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Notes

  1. This class of hybrid metaheuristics includes memetic algorithms.

  2. Also known as evolutionary local search algorithms.

  3. The name is an allusion to Jean Batiste de Lamarck’s contention that phenotype characteristics acquired during lifetime can become heritable traits.

  4. HTH hybrids is referred as multiple interacting walksVerhoeven and Aarts (1995) multi-agent algorithms (Boese 1996), and cooperative search algorithms (Clearwater et al. 1991, 1992; Hogg and Williams 1993; Huberman 1990; Toulouse et al. 1996).

  5. Also known as migration model, diffusion model, and coarse grain model.

  6. The concept of adaptive memory has been proposed in the domain of combinatorial optimization (Taillard et al. 2001). It is similar to the concept of blackboard in the field of Artificial Intelligence (Engelmore 1988).

  7. This procedure is also called meta-evolution.

  8. We consider here a minimization problem.

  9. The term complete is always used in the CP community.

  10. However, the duals cannot be considered.

  11. This scheme is called fitness imitation or fitness inheritance.

  12. Using this design approach, it is worthwhile to speak about hybrid metaheuristics as any metaheuristic will be a hybrid one!.

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  1. CNRS, INRIA, University of Lille 1, Lille, France

    El-Ghazali Talbi

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  1. El-Ghazali Talbi
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Correspondence to El-Ghazali Talbi.

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Talbi, EG. Combining metaheuristics with mathematical programming, constraint programming and machine learning. 4OR-Q J Oper Res 11, 101–150 (2013). https://doi.org/10.1007/s10288-013-0242-3

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  • DOI: https://doi.org/10.1007/s10288-013-0242-3

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