Effective Application of<scp>GRASP</scp>

Festa, Paola; Resende, Maurício G. C.

doi:10.1002/9780470400531.eorms0281

Cited by 7 publications

(5 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was developed by Feo and Resende [10], [20]. Further, GRASP has been used to develop many applications [21], [22]. The two stages of the GRASP algo-rithm are the construction phase and the improvement phase.…”

Section: Grasp Algorithm For Feature Selectionmentioning

confidence: 99%

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Nagpal

Gaur

2017

CIT

View full text Add to dashboard Cite

Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP) for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure) uses a firefly optimization algorithm in the local search optimization phase of the greedy randomized adaptive search procedure (GRASP). Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios) which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FF-GRASP performs better on seven out of nine datasets.

show abstract

Section: Grasp Algorithm For Feature Selectionmentioning

confidence: 99%

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Nagpal

Gaur

2017

CIT

View full text Add to dashboard Cite

show abstract

“…As a multi-start metaheuristic, the GRASP (Greedy Randomized Adaptive Search Procedures) was developed for combinatorial optimization [16,17]. It consists of an iterative process, in each of one, two phases are performed: a construction phase and a local search phase.…”

Section: Grasp Metaheuristicmentioning

confidence: 99%

Robust Optimization for RCPSP Under Uncertainty

Mogaadi¹,

Chaâr²

2016

IJSEA

View full text Add to dashboard Cite

The aim of the present article is to optimize the robustness objective for the Resource-Constrained Project scheduling Problem (RCPSP) dealing with activity duration uncertainty. The studied robustness consists in minimizing the worst-case performance, referred to as the min-max robustness objective, among a set of initial scenarios. We propose an enhanced GRASP approach as a solution to the given scenario-based robust model. This approach is based on different priority rules in the construction phase and a forwardbackward heuristic in the improvement phase. We investigate two different benchmark data sets, the Patterson set and the PSPLIB J30 set. Experiments show that the proposed enhanced GRASP outperforms the basic procedure, and a based-evolutionary algorithm, in robustness optimization.

show abstract

“…The GRASP (Greedy Randomized Adaptive Search Procedures) is a multi-start metaheuristic which was developed for combinatorial optimization [15,16]. It consists of an iterative process, in each of one, two phases are performed: a construction phase and a local search phase.…”

Section: Grasp Metaheuristicmentioning

confidence: 99%

Grasp Approach to RCPSP with MinMax Robustness Objective

Mogaadi¹,

Chaâr²

2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

This paper deals with the Resource-Constrained Project scheduling Problem (RCPSP) under activity duration uncertainty. Based on scenarios, the object is to minimize the worst-case performance among a set of initial scenarios which is referred to as the min-max robustness objective. Due to the complexity of the tackled problem, we propose the application of the GRASP method which is qualified as a simple and effective multi-start metaheuristic. The proposed approach incorporates an adaptive greedy function based on priority rules to construct new solutions, and a local search with a forward-backward heuristic in the improvement phase. Two different benchmark data sets are investigated, the Patterson set and the PSPLIB J30 set. Comparative results show that the proposed enhanced GRASP outperforms the basic procedure in robustness optimization.

show abstract

Effective Application ofGRASP

Cited by 7 publications

References 53 publications

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Robust Optimization for RCPSP Under Uncertainty

Grasp Approach to RCPSP with MinMax Robustness Objective

Contact Info

Product

Resources

About