A biased random-key genetic algorithm for the maximum quasi-clique problem

Título:

A biased random-key genetic algorithm for the maximum quasi-clique problem

Autor:

Isabel Cristina Mello Rosseti

Ano:

2018

DOI:

10.1016/j.ejor.2018.05.071

Revista:

European Journal of Operational Research

ISSN:

03772217

Home:

[doi:10.1016/j.ejor.2018.05.071]

Idioma:

Inglês

Buscar essa publicação no Google Acadêmico

Publicações com a mesma autoria

Improving a state-of-the-art heuristic for the minimum latency problem with data mining

A Branch-and-Cut and MIP-based heuristics for the Prize-Collecting Travelling Salesman Problem

A BRKGA-based matheuristic for the maximum quasi-clique problem with an exact local search strategy

Reactive GRASP for the prize-collecting covering tour problem

A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model

A hybrid data mining heuristic to solve the point¿feature cartographic label placement problem

A Provenance-based heuristic for preserving results confidentiality in cloud-based scientific workflows

Combining integer linear programming with a state-of-the-art heuristic for the 2-path network design problem

Making a state-of-the-art heuristic faster with data mining

Extending the hybridization of metaheuristics with data mining: Dealing with sequences