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Chu, P. C.; Beasley, John E.

doi:10.1023/a:1009642405419

Cited by 649 publications

(90 citation statements)

References 64 publications

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“…Tabu-search methods, as developed by Glover and Kochenberger [32] and further improved by Hanafi and Freville [33] in 1998, were able to solve to optimality all the public instances available so far [21]. At the same year, Chu and Beasley [34] proposed the first competitive GA for the MKP and they also made available a well-designed instance set, containing large correlated instances (n ¼ 100; 250; 500 and m ¼ 5; 10; 30). Several of their results for these instances were considerably improved by Vasquez [35,36], through a tabu-search algorithm.…”

Section: The Multidimensional Knapsack Problemmentioning

confidence: 99%

“…Indeed, Kan et al [61] showed the connections between solutions produced by a greedy heuristic using dual relevance values and the primal solution of the linear relaxation of the MKP. A similar greedy heuristic was applied by Chu and Beasley [34] (as discussed in Section 2.1.2). Later on, Puchinger et al [22] verified the superiority of e dual …”

Section: Efficiencies and The Core Conceptmentioning

confidence: 99%

“…However, what seems to be the most promising approach was only proposed in 1998 by Chu and Beasley [34,40] (CBGA), and its framework has been intensely used as reference for development of new GAs. Indeed, more recent analysis have proved the quality of the CBGA.…”

Section: Chu and Beasley Genetic Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

2014

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Section: The Multidimensional Knapsack Problemmentioning

confidence: 99%

Section: Efficiencies and The Core Conceptmentioning

confidence: 99%

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On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

2014

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“…[18] formulate WDP as a multi dimensional knapsack problem. This problem has been studied extensively in the domain of operations research [19] including using genetic algorithms [20]. However all of these approaches can not be applied directly to the MWDP as it is formulated as a generalization of the WDP.…”

Section: Related Workmentioning

confidence: 99%

Scalable Grid Resource Trading with Greedy Heuristics

Buss

Lee

Veit

2010

2010 International Conference on Complex, Intelligent and Software Intensive Systems

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Abstract-As Grid infrastructures become more widely used by the academic and commercial world, the problem of resource allocation increases in complexity. Resource trading markets are one mechanism that allows many resource owners and resource consumers to trade. To perform efficiently trading markets for grids require approaches to match consumers and producers. Solutions for optimal and non-optimal resource trading exist, but fail to scale effectively to meet the challenges of large numbers of traders. This paper first defines the problem of scalable resource trading in grids before describing and evaluating greedy approaches for scalability.

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“…In addition and in order to guarantee the satisfaction of the constraints of problem, (mainly the first group of constraints that imposes not exceeding the breaks duration) we used a "repair" procedure, inspired in that of Chu and Beasley [7] for a multiple knapsack problem. This procedure has two phases: in the first one we guarantee that the break time is not exceeded by removing spots, whenever needed, while in the second phase we try to satisfy spot's number of viewers, or improve the solution, by adding spots to the breaks which have some unused time.…”

Section: The Proposed Genetic Algorithmmentioning

confidence: 99%

A Genetic Algorithm Approach for the TV Self-Promotion Assignment Problem

Pereira¹,

Fontes²,

Fontes³

et al. 2009

AIP Conference Proceedings

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Abstract. We report on the development of a Genetic Algorithm (GA), which has been integrated into a Decision Support System to plan the best assignment of the weekly self-promotion space for a TV station. The problem addressed consists on deciding which shows to advertise and when such that the number of viewers, of an intended group or target, is maximized. The GA proposed incorporates a greedy heuristic to find good initial solutions. These solutions, as well as the solutions later obtained through the use of the GA, go then through a repair procedure. This is used with two objectives, which are addressed in turn. Firstly, it checks the solution feasibility and if unfeasible it is fixed by removing some shows. Secondly, it tries to improve the solution by adding some extra shows. Since the problem faced by the commercial TV station is too big and has too many features it cannot be solved exactly. Therefore, in order to test the quality of the solutions provided by the proposed GA we have randomly generated some smaller problem instances. For these problems we have obtained solutions on average within 1% of the optimal solution value.

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Untitled

Cited by 649 publications

References 64 publications

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem

Scalable Grid Resource Trading with Greedy Heuristics

A Genetic Algorithm Approach for the TV Self-Promotion Assignment Problem

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