A Genetic Algorithm for the Set Covering Problem

By K. Al-Sultan, M. F. Hussain, J S Nizami. 1996

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Citations in the corpus

Genetic CNN (2017, positive): “It is commonly used to generate high-quality solutions to optimization and search problems [14][30][2][4] by performing bio-inspired operators such as mutation, crossover and selection.”
An indirect Genetic Algorithm for a nurse-scheduling problem (2004, unknown): “More promising seem approaches such as used by Beasley and Chu [19] that bias the way the solution space is sampled towards feasible areas.”
Genetic Algorithms + Data Structures = Evolution Programs (1996, methodology): “Beasley [31] experimented with modified genetic algorithm on many SCPs, from 200 rows by 1,000 columns to 1,000 rows by 10,000 columns.”
A Gentle Introduction to Memetic Algorithms (2003, methodology): “A remarkable history of successes has been reported with respect to the application of MAs to N P−hard problems such as the following: Graph Partitioning [18] [19] [139] [142] [143], Min Number Partitioning [14], Max Independent Set [2] [90] [200], Bin-Packing [195], Min Graph Coloring [41] [64] [60”
Covering models and optimization techniques for emergency response facility location and planning: a review (2011, methodology): “Non-unicost LSCP GA Beasley and Chu (1996) MCLP LR Galvao and ReVelle (1996)”
Multi-robot coalition formation (2006, background): “However numerous heuristic based SPP algorithms have been proposed in the literature (Chu and Beasley, 1996; Fisher and Kedia, 1990; Balas and Padberg, 1976), although it remains to be seen whether such heuristic algorithms are applicable to MRTA problems.”
On the use of genetic algorithms to solve location problems (2002, methodology): “In the two papers by Beasley and Chu [8] and Lorena and de Souza-Lopez [9], the authors have focussed on the application of GAs to set covering problems.”
Heuristics for cardinality constrained portfolio optimisation (2000, methodology): “Note here that the strategy adopted in Algorithm 1, namely to change (if possible) the GA solution into a feasible solution for the original problem, is a strategy that we have used, with success, in our previous GA work [7,13].”
A multiperiod set covering location model for dynamic redeployment of ambulances (2008, background): “Because of the complex combinatorial nature of these problems, there have been various successful attempts to identify near-optimal solutions through the use of meta-heuristic search methods [33] such as; tabu search [10,34,35]; simulated annealing [31,36]; and evolutionary algorithms [8,9,37,38].”
Algorithms for the Set Covering Problem (2000, unknown): “[25].”