Genetic Algorithm Based on Greedy Strategy in the 0-1 Knapsack Problem

Zhao, Jiangfei; Huang, Ting-lei; Pang, Fei; Liu, Yuanjie

doi:10.1109/wgec.2009.43

Cited by 39 publications

(19 citation statements)

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“…The problem is NP-hard for the general case. Hybrid algorithms, composed of genetic algorithms and local search, have presented interesting results for this problem [20]. Variation of this problem can be found in different real-world applications, e.g., in project selection and economic planning [21,22].…”

Section: -1 Knapsack Problemmentioning

confidence: 99%

Bayesian partition crossover for pseudo-Boolean optimization

Laërtius¹,

Tinós²

2019

Learn. Nonlin. Mod.

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The recombination of solutions is important for population metaheuristics and other optimization algorithms. Recently, an efficient recombination operator that preserve the interaction between the decision variables was proposed for pseudo-Boolean optimization. Partition Crossover (PX) groups decision variables in order to allow the decomposition of the evaluation function. PX allows to find, with computational cost proportional to the cost of evaluating one solution of the problem, the best solution among a number of offspring solutions that grows exponentially with the number of recombining components found by the operator. PX has been so far used only in problems where the information about the linkage between the decision variables is known a priori. This information is stored in a graph, know as variable interaction graph. We propose a new PX for pseudo-Boolean optimization problems that can be used when the variable interaction graph is not known a priori. For this purpose, it is necessary to estimate the linkage between the decision variables by using procedures generally employed in estimation of distribution algorithms. The experimental results show that the new recombination operator generally improves the number of offspring that are better than their parents when compared to traditional recombination operators. However, generating better offspring does not necessarily imply in better performance for the evolutionary algorithm.

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Section: -1 Knapsack Problemmentioning

confidence: 99%

Bayesian partition crossover for pseudo-Boolean optimization

Laërtius¹,

Tinós²

2019

Learn. Nonlin. Mod.

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“…Silhouette provides a succinct graphical representation of how well each object lies within its cluster 5,8 . Silhouette value is calculated by Eq.…”

Section: Silhouettementioning

confidence: 99%

On-line Rule Updating System Using Evolutionary Computation for Managing Distributed Database

Wedashwara¹,

Mabu²,

Obayashi³

et al. 2015

JRNAL

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This research proposes a decision support system of database cluster optimization using genetic network programming (GNP) with on-line rule based clustering. GNP optimizes cluster quality by reanalyzing weak points of each cluster and maintaining rules stored in each cluster. The maintenance of rules includes: 1) adding new relevant rules; 2) moving rules between clusters; and 3) removing irrelevant rules. The simulations focus on optimizing cluster quality response against several unbalanced data growth to the data-set that is working with storage rules. The simulation results of the proposed method show its priority comparing to GNP rule based clustering without on-line optimization.

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“…As a consequence, there are also heuristics that lead to suboptimal solutions through different approaches as that can be summarized as follows: heuristics based on the dimensional reduction of the problem admissible solution set [9], methods relying on the graceful degradation concept [10], techniques adopting genetic algorithms [11] and strategies based on greedy approaches [12]. It has been reported in Procedure 1 [14] the proposed heuristic that allow a greedy approach for solving the RAP.…”

Section: B Resource Allocation Algorithmmentioning

confidence: 99%

An Optimized Resource Allocation Scheme Based on a Multidimensional Multiple-Choice Approach with Reduced Complexity

Bartoli

Tassi

Marabissi

et al. 2011

2011 IEEE International Conference on Communications (ICC)

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Long Term Evolution (LTE) is considered one of the main candidate to provide wireless broadband access to mobile users. Among main LTE characteristics, flexibility and efficiency can be guaranteed by resorting to suitable resource allocation schemes, in particular by adopting adaptive OFDM schemes. This paper proposes a novel solution to the subcarrier allocation problem for the LTE downlink that takes into account the queues length, the QoS constraints and the channel conditions. Each user has different queues, one for each QoS class, and can transmit with a different data rate depending on the propagation conditions. The proposed algorithm defines a value of each possible sub-carrier assignment as a linear combination of all the inputs following a cross-layer approach. The problem is formulated as a Multidimensional Multiplechoice Knapsack Problem (MMKP) whose optimal solution is not feasible for our purposes due to the too long computing time required to find it. Hence, a novel efficient heuristic has been proposed to solve the problem. Results shows good performance of the proposed resource allocation scheme both in terms of throughput and delay while guarantees fairness among the users. Performance has been compared also with fixed allocation scheme and round robin.

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Genetic Algorithm Based on Greedy Strategy in the 0-1 Knapsack Problem

Cited by 39 publications

References 0 publications

Bayesian partition crossover for pseudo-Boolean optimization

Bayesian partition crossover for pseudo-Boolean optimization

On-line Rule Updating System Using Evolutionary Computation for Managing Distributed Database

An Optimized Resource Allocation Scheme Based on a Multidimensional Multiple-Choice Approach with Reduced Complexity

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