Automatic Citation Contextualization Based Scientific Document Summarization Using Multi-objective Differential Evolution

2021 ◽  
pp. 289-301
Author(s):  
Dipanwita Debnath ◽  
Ranjita Das
Keyword(s):  
Differential Evolution ◽  
Document Summarization ◽  
Multi Objective ◽  
Scientific Document Summarization

Scientific document summarization in multi-objective clustering framework

2021 ◽  
Author(s):  
Santosh Kumar Mishra ◽  
Naveen Saini ◽  
Sriparna Saha ◽  
Pushpak Bhattacharyya
Keyword(s):  
Document Summarization ◽  
Multi Objective ◽  
Scientific Document Summarization

NARMAX Model Identification Using Multi-Objective Optimization Differential Evolution

2018 ◽  
Vol 10 (7) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
◽  
Zakwan Mansor ◽  
Azuwir Mohd Nor ◽  
Mohd Sazli Saad ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Model Identification ◽  
Multi Objective Optimization ◽  
Multi Objective

PERTURBATION PARAMETERS TUNING OF MULTI-OBJECTIVE OPTIMIZATION DIFFERENTIAL EVOLUTION AND ITS APPLICATION TO DYNAMIC SYSTEM MODELING

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mohd Zakimi Zakaria ◽  
Hishamuddin Jamaluddin ◽  
Robiah Ahmad ◽  
Azmi Harun ◽  
Radhwan Hussin ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Dynamic System ◽  
Dynamic Systems ◽  
System Modeling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Structure Selection ◽  
Dynamic System Modeling ◽  
Perturbation Parameters ◽  
Model Structure Selection

This paper presents perturbation parameters for tuning of multi-objective optimization differential evolution and its application to dynamic system modeling. The perturbation of the proposed algorithm was composed of crossover and mutation operators.  Initially, a set of parameter values was tuned vigorously by executing multiple runs of algorithm for each proposed parameter variation. A set of values for crossover and mutation rates were proposed in executing the algorithm for model structure selection in dynamic system modeling. The model structure selection was one of the procedures in the system identification technique. Most researchers focused on the problem in selecting the parsimony model as the best represented the dynamic systems. Therefore, this problem needed two objective functions to overcome it, i.e. minimum predictive error and model complexity.  One of the main problems in identification of dynamic systems is to select the minimal model from the huge possible models that need to be considered. Hence, the important concepts in selecting good and adequate model used in the proposed algorithm were elaborated, including the implementation of the algorithm for modeling dynamic systems. Besides, the results showed that multi-objective optimization differential evolution performed better with tuned perturbation parameters.

An easy‐to‐implement differential evolution approach for multi‐objective optimizations

2006 ◽  
Vol 23 (2) ◽  
pp. 124-138 ◽  
Author(s):  
Hui‐Yuan Fan ◽  
Jouni Lampinen ◽  
Yeshayahou Levy
Keyword(s):  
Differential Evolution ◽  
Multi Objective ◽  
Evolution Approach
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