which is known as the quadratic 0–1 knapsack problem. Glover
et al. (1995) apply this model to the protection of cranes. They consider
14 species of endangered cranes, mainly due to the destruction
of wetlands. The hunt for the Nordic races, and pesticides in
Africa, also contribute to their decline. There are several approaches
to measure the dissimilarity between two species. Glover et al.
(1995) use data on the differences between DNA sequences associated
with the two species and obtained by a molecular biology technique,
DNA hybridization. Regarding the 14 crane species
considered, Glover et al. (1995) use genetic distances established
by Krajewski (1989). There are several ways to solve (P38). The program
can be directly submitted to a solver such as CPLEX (2007).
One can also use one of several methods that have been proposed
over the last thirty years for quadratic programming in 0–1 variables
(see Section 2.3.1) or specific algorithms developed for the
quadratic knapsack problem (see e.g., Kellerer et al., 2004). Glover
et al. (1995) use a classical linearization of (P38). Lozano et al.
(2011) propose an iterative greedy algorithm for this classical problem
of combinatorial optimization that has proved to be effective
compared to other metaheuristics
which is known as the quadratic 0–1 knapsack problem. Gloveret al. (1995) apply this model to the protection of cranes. They consider14 species of endangered cranes, mainly due to the destructionof wetlands. The hunt for the Nordic races, and pesticides inAfrica, also contribute to their decline. There are several approachesto measure the dissimilarity between two species. Glover et al.(1995) use data on the differences between DNA sequences associatedwith the two species and obtained by a molecular biology technique,DNA hybridization. Regarding the 14 crane speciesconsidered, Glover et al. (1995) use genetic distances establishedby Krajewski (1989). There are several ways to solve (P38). The programcan be directly submitted to a solver such as CPLEX (2007).One can also use one of several methods that have been proposedover the last thirty years for quadratic programming in 0–1 variables(see Section 2.3.1) or specific algorithms developed for thequadratic knapsack problem (see e.g., Kellerer et al., 2004). Gloveret al. (1995) use a classical linearization of (P38). Lozano et al.(2011) propose an iterative greedy algorithm for this classical problemof combinatorial optimization that has proved to be effectivecompared to other metaheuristics
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which is known as the quadratic 0–1 knapsack problem. Glover
et al. (1995) apply this model to the protection of cranes. They consider
14 species of endangered cranes, mainly due to the destruction
of wetlands. The hunt for the Nordic races, and pesticides in
Africa, also contribute to their decline. There are several approaches
to measure the dissimilarity between two species. Glover et al.
(1995) use data on the differences between DNA sequences associated
with the two species and obtained by a molecular biology technique,
DNA hybridization. Regarding the 14 crane species
considered, Glover et al. (1995) use genetic distances established
by Krajewski (1989). There are several ways to solve (P38). The program
can be directly submitted to a solver such as CPLEX (2007).
One can also use one of several methods that have been proposed
over the last thirty years for quadratic programming in 0–1 variables
(see Section 2.3.1) or specific algorithms developed for the
quadratic knapsack problem (see e.g., Kellerer et al., 2004). Glover
et al. (1995) use a classical linearization of (P38). Lozano et al.
(2011) propose an iterative greedy algorithm for this classical problem
of combinatorial optimization that has proved to be effective
compared to other metaheuristics
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