Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Guided Tour of Artificial Intelligence ResearchMeta-heuristics and Artificial Intelligence

A Guided Tour of Artificial Intelligence Research: Meta-heuristics and Artificial Intelligence [Meta-heuristics are generic search methods that are used to solve challenging combinatorial problems. We describe theseHao, Jin-Kao 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-heuristicsthatSolnon, Christine 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.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Guided Tour of Artificial Intelligence ResearchMeta-heuristics and Artificial Intelligence

Editors: Marquis, Pierre; Papini, Odile; Prade, Henri
Hao, Jin-Kao; Solnon, Christine
A Guided Tour of Artificial Intelligence Research — May 8, 2020
Download PDF
25 pages

Loading next page...
 
/lp/springer-journals/a-guided-tour-of-artificial-intelligence-research-meta-heuristics-and-3tiEFt1VtG

References (98)

Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-06166-1
Pages
27 –52
DOI
10.1007/978-3-030-06167-8_2
Publisher site
See Chapter on Publisher Site

Abstract

[Meta-heuristics are generic search methods that are used to solve challenging combinatorial problems. We describe theseHao, Jin-Kao 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-heuristicsthatSolnon, Christine 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.]

Published: May 8, 2020

There are no references for this article.