scholarly journals INTER AND INTRA CLUSTER ON SELF-ADAPTIVE DIFFERENTIAL EVOLUTION FOR MULTI-DOCUMENT SUMMARIZATION

2018 ◽  
Vol 11 (2) ◽  
pp. 86
Author(s):  
Alifia Puspaningrum ◽  
Adhi Nurilham ◽  
Eva Firdayanti Bisono ◽  
Khoirul Umam ◽  
Agus Zainal Arifin
Keyword(s):  
Cluster Analysis ◽  
Differential Evolution ◽  
Text Analysis ◽  
Experimental Results ◽  
Adaptive Differential Evolution ◽  
Content Coverage ◽  
Different Content ◽  
F Measure ◽  
Self Adaptive

Multi – document as one of summarization type has become more challenging issue than single-document because its larger space and its different content of each document. Hence, some of optimization algorithms consider some criteria in producing the best summary, such as relevancy, content coverage, and diversity. Those weighted criteria based on the assumption that the multi-documents are already located in the same cluster. However, in a certain condition, multi-documents consist of many categories and need to be considered too. In this paper, we propose an inter and intra cluster which consist of four weighted criteria functions (coherence, coverage, diversity, and inter-cluster analysis) to be optimized by using SaDE (Self Adaptive Differential Evolution) to get the best summary result. Therefore, the proposed method will deal not only with the value of compactness quality of the cluster within but also the separation of each cluster. Experimental results on Text Analysis Conference (TAC) 2008 datasets yields better summaries results with average ROUGE-1 on precision, recall, and f - measure 0.77, 0.07, and 0.12 compared to another method that only consider the analysis of intra-cluster.

scholarly journals Differential Evolution for Neural Networks Optimization

Mathematics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 69 ◽  
Author(s):  
Marco Baioletti ◽  
Gabriele Di Bari ◽  
Alfredo Milani ◽  
Valentina Poggioni
Keyword(s):  
Neural Networks ◽  
Comparative Study ◽  
Differential Evolution ◽  
Adaptive Methods ◽  
Experimental Results ◽  
Classification Problems ◽  
Adaptive Differential Evolution ◽  
Neural Networks Optimization ◽  
Better Than ◽  
Self Adaptive

In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer strategy. Moreover, a new crossover called interm is proposed, and a new self-adaptive version of DE called MAB-ShaDE is suggested to reduce the number of parameters. The framework has been tested on some well-known classification problems and a comparative study on the various combinations of self-adaptive methods, mutation, and crossover operators available in literature is performed. Experimental results show that DENN reaches good performances in terms of accuracy, better than or at least comparable with those obtained by backpropagation.

scholarly journals PEMBOBOTAN KATA BERDASARKAN KLASTER PADA OPTIMISASI COVERAGE, DIVERSITY DAN COHERENCE UNTUK PERINGKASAN MULTI DOKUMEN

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Ryfial Azhar ◽  
Muhammad Machmud ◽  
Hanif Affandi Hartanto ◽  
Agus Zainal Arifin ◽  
Diana Purwitasari
Keyword(s):  
Differential Evolution ◽  
Text Analysis ◽  
Additional Term ◽  
Optimization Method ◽  
Term Weighting ◽  
High Coverage ◽  
Document Summarization ◽  
Adaptive Differential Evolution ◽  
Coherence Level ◽  
Self Adaptive

[Id]Peringkasan yang baik dapat diperoleh dengan coverage, diversity dan coherence yang optimal. Namun, terkadang sub-sub topik yang terkandug dalam dokumen tidak terekstrak dengan baik, sehingga keterwakilan setiap sub-sub topik tersebut tidak ada dalam hasil peringkasan dokumen. Pada paper ini diusulkan metode baru pembobotan kata berdasarkan klaster pada optimisasi coverage, diversity dan coherence untuk peringkasan multi-dokumen. Metode optimasi yang digunakan ialah self-adaptive differential evolution (SaDE) dengan penambahan pembobotan kata berdasarkan hasil dari pembentukan cluster dengan metode Similarity Based Histogram Clustering (SHC). Metode SHC digunakan untuk mengklaster kalimat sehingga setiap sub-topik pada dokumen bisa terwakili dalam hasil peringkasan. Metode SaDE digunakan untuk mencari solusi hasil ringkasan yang memiliki tingkat coverage, diversity, dan coherence paling tinggi. Uji coba dilakukan pada 15 topik dataset Text Analysis Conference (TAC) 2008. Hasil uji coba menunjukkan bahwa metode yang diusulkan dapat menghasilkan ringkasan skor ROUGE-1 sebesar 0.6704, ROUGE-2 sebesar 0.2051, ROUGE-L sebesar 0.6271 dan ROUGE-SU sebesar 0.3951.Kata kunci : peringkasan multi dokumen, similarity based histogram clustering, coverage, diversity, coherence[En]Good summary can be obtained with optimizing coverage, diversity, and coherence. Nevertheless, sometime sub-topics wich is contained in the document is not extracted well, so that the representation of each sub-topic is appear in docment summarizarion result. In this paper, we propose new of term weighting based on? cluster in optimizing coverage, diversity, and coherence for multi-document summarization. Optimization method which is used is self-adaptive differential evolution (SaDE) with additional term weighting based on clustering result with Similarity Based Histogram Clustering (SHC). SHC is used to cluster sentence so that every sub-topic in the document can be represented in summarization result. SaDE is used to search summarization result solution which has high coverage, diversity, and coherence level. Experiment is done on 15 topics in Text Analysis Conference (TAC) 2008 dataset. Experimental results show that this proposed method can produce summarization score? ROUGE-1 0.6704, ROUGE-2 0.2051, ROUGE-L 0.6271 and ROUGE-SU 0.3951.Keywords: multy-document summarization, similarity based histogram clustering, coverage, diversity, coherence.

scholarly journals Comparative performance evaluation of self-adaptive differential evolution with GA, SCE and DE algorithms for the automatic calibration of a computationally intensive distributed hydrological model

2020 ◽  
Author(s):  
Saswata Nandi ◽  
M. Janga Reddy
Keyword(s):  
Differential Evolution ◽  
Hydrological Model ◽  
Automatic Calibration ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Infiltration Capacity ◽  
Vic Model ◽  
Adaptive Differential Evolution ◽  
Computationally Intensive ◽  
Self Adaptive

Abstract Recently, physically-based hydrological models have been gaining much popularity in various activities of water resources planning and management, such as assessment of basin water availability, floods, droughts, and reservoir operation. Every hydrological model contains some parameters that must be tuned to the catchment being studied to obtain reliable estimates from the model. This study evaluated the performance of different evolutionary algorithms, namely genetic algorithm (GA), shuffled complex evolution (SCE), differential evolution (DE), and self-adaptive differential evolution (SaDE) algorithm for the parameter calibration of a computationally intensive distributed hydrological model, variable infiltration capacity (VIC) model. The methodology applied and tested for a case study of the upper Tungabhadra River basin in India, and the performance of the algorithms is evaluated in terms of reliability, variability, efficacy measures in a limited number of function evaluations, their ability for achieving global convergence, and also by their capability to produce a skillful simulation of streamflows. The results of the study indicated that SaDE facilitates an effective calibration of the VIC model with higher reliability and faster convergence to optimal solutions as compared to the other methods. Moreover, due to the simplicity of the SaDE, it provides easy implementation and flexibility for the automatic calibration of complex hydrological models.

scholarly journals A face recognition framework using self-adaptive differential evolution

2019 ◽  
Vol 17 (2) ◽  
pp. 4-14
Author(s):  
Guilherme Felippe Plichoski ◽  
Chidambaram Chidambaram ◽  
Rafael Stubs Parpinelli
Keyword(s):  
Face Recognition ◽  
Differential Evolution ◽  
Adaptive Differential Evolution ◽  
Self Adaptive
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