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Meta-heuristics and Artificial Intelligence

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A Guided Tour of Artificial Intelligence Research

Abstract

Meta-heuristics are generic search methods that are used to solve challenging combinatorial problems. We describe these methods and highlight their common features and differences by grouping them in two main kinds of approaches: Perturbative meta-heuristics that build new combinations by modifying existing combinations (such as, for example, genetic algorithms and local search), and Constructive meta-heuristics that generate new combinations in an incremental way by using a stochastic model (such as, for example, estimation of distribution algorithms and ant colony optimization). These approaches may be hybridised, and we describe some classical hybrid schemes. We also introduce the notions of diversification (exploration) and intensification (exploitation), which are shared by all these meta-heuristics: diversification aims at ensuring a good sampling of the search space and, therefore, at reducing the risk of ignoring a promising sub-area which actually contains high-quality solutions, whereas intensification aims at locating the best combinations within a limited region of the search space. Finally, we describe two applications of meta-heuristics to typical artificial intelligence problems: satisfiability of Boolean formulas, and constraint satisfaction problems.

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Authors and Affiliations

  1. LERIA, Université d’Angers, 2 Boulevard Lavoisier, 49045, Angers, France

    Jin-Kao Hao

  2. LIRIS-CNRS, INSA de Lyon, 20 Avenue Albert Einstein, 69621, Villeurbanne, France

    Christine Solnon

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  1. Jin-Kao Hao
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  2. Christine Solnon
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Correspondence to Jin-Kao Hao .

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Editors and Affiliations

  1. CRIL-CNRS, Université d’Artois and Institut Universitaire de France, Lens, France

    Pierre Marquis

  2. Aix Marseille Université, Université de Toulon, CNRS, LIS, Marseille, France

    Odile Papini

  3. IRIT, CNRS and Université Paul Sabatier, Toulouse, France

    Henri Prade

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Hao, JK., Solnon, C. (2020). Meta-heuristics and Artificial Intelligence. In: Marquis, P., Papini, O., Prade, H. (eds) A Guided Tour of Artificial Intelligence Research. Springer, Cham. https://doi.org/10.1007/978-3-030-06167-8_2

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