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Hybrid Metaheuristics pp 3–76Cite as

A Unified Taxonomy of Hybrid Metaheuristics with Mathematical Programming, Constraint Programming and Machine Learning

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 434))

Abstract

Over the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization. The best results found for many real-life or classical optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming, constraint programming and machine learning techniques have provided very powerful search algorithms. Four different types of combinations are considered in this chapter:

  • Combining metaheuristics with (complementary) metaheuristics.

  • Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in operations research.

  • Combining metaheuristics with constraint programming approaches developed in the artificial intelligence community.

  • Combining metaheuristics with machine learning and data mining techniques.

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Talbi, EG. (2013). A Unified Taxonomy of Hybrid Metaheuristics with Mathematical Programming, Constraint Programming and Machine Learning. In: Talbi, EG. (eds) Hybrid Metaheuristics. Studies in Computational Intelligence, vol 434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30671-6_1

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