Hybridization of GRASP Metaheuristic with Data Mining Techniques

Ribeiro, Marcos Henrique; Plastino, Alexandre; Martins, Simone L.

doi:10.1007/s10852-005-9030-1

Cited by 61 publications

(39 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of works rely on the Apriori algorithm (to identify interesting subsolutions) [65][66][67][68] or on CBR [69]. Another important issue in PMs is the population diversity, since maintaining it may lead to better performances.…”

Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

View full text Add to dashboard Cite

This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

show abstract

Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Hybrid evolutionary metaheuristics (HEMH) [77] presents a combination with different metaheuristics, each one integrated to other to improve the search capabilities. The hybridization of greedy randomized adaptive search procedure (GRASP) with data mining (DM-GRASP) [78] is directed to a primary set with high quality solutions discrete along the Pareto front within the framework of MOEA/D.…”

Section: ------------------------------------------------------------mentioning

confidence: 99%

Oil in Syria between Terrorism and Dictatorship

Kurada¹,

Pavan²,

Rao³

2017

bfup

View full text Add to dashboard Cite

show abstract

“…The hybridization of GRASP with data mining techniques was introduced by Ribeiro et al (2006). This scheme uses a data mining algorithm to search for solution patterns that occur in high-quality elite solutions produced by the basic GRASP algorithm.…”

Section: Extensionsmentioning

confidence: 99%

GRASP: Greedy Randomized Adaptive Search Procedures

Resende

Ribeiro

2013

Search Methodologies

View full text Add to dashboard Cite

others. The method described in this chapter represents another example of such a technique. Metaheuristics are based on distinct paradigms and offer different mechanisms to escape from locally optimal solutions. They are among the most effective solution strategies for solving combinatorial optimization problems in practice and have been applied to a wide array of academic and real-world problems. The customization (or instantiation) of a metaheuristic to a given problem yields a heuristic for that problem.In this chapter, we consider the combinatorial optimization problem of minimizing f (S) over all solutions S ∈ X, which is defined by a finite set E = {e 1 ,..., e n } (called the ground set), by a set of feasible solutions X ⊆ 2 E and by an objective function f : 2 E → R. The ground set E, the objective function f , and the constraints defining the set of feasible solutions X are specific for each problem. We seek an optimal solution S * ∈ X such that f (S * ) ≤ f (S), ∀S ∈ X.GRASP, which stands for greedy randomized adaptive search procedures Resende 1989, 1995), is a multistart, or iterative, metaheuristic in which

show abstract

Hybridization of GRASP Metaheuristic with Data Mining Techniques

Cited by 61 publications

References 15 publications

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Oil in Syria between Terrorism and Dictatorship

GRASP: Greedy Randomized Adaptive Search Procedures

Contact Info

Product

Resources

About