Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227085
Isabella Maria Alonso Gomes, N. T. Roman
Distributional models have become popular due to the abstractions that allowed their immediate use, with good results and little implementation effort when compared to precursor models. Given their presumed high level of generalization it would be expected that good and similar results would be found in data sets sharing the same nature and purpose. However, this is not always the case. In this work, we present the results of the application of BERTimbau in two related data sets, built for the task of Semantic Similarity identification, with the goal of detecting redundancy in text. Results showed that there are considerable differences in accuracy between the data sets. We explore aspects of the data sets that could explain why accuracy results are different across them.
{"title":"How aspects of similar datasets can impact distributional models","authors":"Isabella Maria Alonso Gomes, N. T. Roman","doi":"10.5753/eniac.2022.227085","DOIUrl":"https://doi.org/10.5753/eniac.2022.227085","url":null,"abstract":"Distributional models have become popular due to the abstractions that allowed their immediate use, with good results and little implementation effort when compared to precursor models. Given their presumed high level of generalization it would be expected that good and similar results would be found in data sets sharing the same nature and purpose. However, this is not always the case. In this work, we present the results of the application of BERTimbau in two related data sets, built for the task of Semantic Similarity identification, with the goal of detecting redundancy in text. Results showed that there are considerable differences in accuracy between the data sets. We explore aspects of the data sets that could explain why accuracy results are different across them.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115382812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227586
Herbert da Silva Costa, Anderson Cordeiro Cardoso, Cristiane Mendes Netto, D. C. Martins-Jr, S. Simões
Um desafio na modalidade EaD é combater a evasão, que segundo a ABED, varia entre 21 e 50%. Para este fim, diversos métodos de mineração de dados foram aplicados, utilizando dados de interações dos alunos no AVA. Contudo, um problema relevante é selecionar as melhores características (variáveis/atributos) para predição da evasão. Neste artigo, propomos um arcabouço que utiliza métodos de explicabilidade (XAI-SHAP) para selecionar atributos com maior poder preditivo em VLE que utilizam CMS terceirizados. Após a seleção, o modelo proposto alcançou resultados de recall 0,96 e precision 0,95, compatíveis com o estado da arte, porém utilizando um conjunto menor de atributos e uma base de dados com menor número de instâncias.
{"title":"A Framework for prediction of dropout in distance learning through XAI techniques in Virtual Learning Environment","authors":"Herbert da Silva Costa, Anderson Cordeiro Cardoso, Cristiane Mendes Netto, D. C. Martins-Jr, S. Simões","doi":"10.5753/eniac.2022.227586","DOIUrl":"https://doi.org/10.5753/eniac.2022.227586","url":null,"abstract":"Um desafio na modalidade EaD é combater a evasão, que segundo a ABED, varia entre 21 e 50%. Para este fim, diversos métodos de mineração de dados foram aplicados, utilizando dados de interações dos alunos no AVA. Contudo, um problema relevante é selecionar as melhores características (variáveis/atributos) para predição da evasão. Neste artigo, propomos um arcabouço que utiliza métodos de explicabilidade (XAI-SHAP) para selecionar atributos com maior poder preditivo em VLE que utilizam CMS terceirizados. Após a seleção, o modelo proposto alcançou resultados de recall 0,96 e precision 0,95, compatíveis com o estado da arte, porém utilizando um conjunto menor de atributos e uma base de dados com menor número de instâncias.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114658156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227622
M. A. S. D. Silva, L. N. Matos, F. E. O. Santos, M. H. Dompieri, F. Moura
This article compares the clustering of Brazilian municipalities according to their agricultural diversity using two approaches, one based on feature engineering and the other based on feature extraction using Deep Learning based on autoencoders and cluster analysis based on k-means and Self-Organizing Maps. The analyzes were conducted from panel data referring to IBGE’s annual estimates of Brazilian agricultural production between 1999 and 2018. Different structures of simple stacked undercomplete autoencoders were analyzed, varying the number of layers and neurons in each of them, including the latent layer. The asymmetric exponential linear loss function was also evaluated to cope with the sparse data. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the k-means presented a superior result than the clustering of the raw data from the k-means, demonstrating the ability of simple autoencoders to represent from their latent layer important features of the data. Although the general accuracy is low, the results are promising, considering that we evaluated the most simple strategy for Deep Clustering.
{"title":"Feature engineering vs. extraction: clustering Brazilian municipalities through spatial panel agricultural data via autoencoders","authors":"M. A. S. D. Silva, L. N. Matos, F. E. O. Santos, M. H. Dompieri, F. Moura","doi":"10.5753/eniac.2022.227622","DOIUrl":"https://doi.org/10.5753/eniac.2022.227622","url":null,"abstract":"This article compares the clustering of Brazilian municipalities according to their agricultural diversity using two approaches, one based on feature engineering and the other based on feature extraction using Deep Learning based on autoencoders and cluster analysis based on k-means and Self-Organizing Maps. The analyzes were conducted from panel data referring to IBGE’s annual estimates of Brazilian agricultural production between 1999 and 2018. Different structures of simple stacked undercomplete autoencoders were analyzed, varying the number of layers and neurons in each of them, including the latent layer. The asymmetric exponential linear loss function was also evaluated to cope with the sparse data. The results show that in comparison with the ground truth adopted, the autoencoder model combined with the k-means presented a superior result than the clustering of the raw data from the k-means, demonstrating the ability of simple autoencoders to represent from their latent layer important features of the data. Although the general accuracy is low, the results are promising, considering that we evaluated the most simple strategy for Deep Clustering.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127347483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227315
Pedro Francis Lopes, Amilton dos Santos Júnior, R. Azevedo, P. Dalgalarrondo, A. Fioravanti
Quality of life is an essential metric for evaluating the well-being of students. This work investigates the viability of a model to predict a WHOQoL-Bref answer based on other answers and the overall domain and average scores. For that, we use data from an extensive pooling done with undergraduate students in Brazil (UNICAMP), gathered between 2017 and 2018. We also discuss model types and hyperparameter effects on model evaluation metrics. Finally, we conclude that it is possible to create a model to predict the esteem question - which is the most correlated with the average domain score with the data sample available.
{"title":"Aspects of a learned model to predict the quality of life of university students in Brazil","authors":"Pedro Francis Lopes, Amilton dos Santos Júnior, R. Azevedo, P. Dalgalarrondo, A. Fioravanti","doi":"10.5753/eniac.2022.227315","DOIUrl":"https://doi.org/10.5753/eniac.2022.227315","url":null,"abstract":"Quality of life is an essential metric for evaluating the well-being of students. This work investigates the viability of a model to predict a WHOQoL-Bref answer based on other answers and the overall domain and average scores. For that, we use data from an extensive pooling done with undergraduate students in Brazil (UNICAMP), gathered between 2017 and 2018. We also discuss model types and hyperparameter effects on model evaluation metrics. Finally, we conclude that it is possible to create a model to predict the esteem question - which is the most correlated with the average domain score with the data sample available.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227548
Giuseppe F. Neto, Pericles Miranda, R. F. Mello, André C. A. Nascimento
A distribuição de candidatos em locais de provas é um problema logístico relevante e afeta diversos países, inclusive o Brasil, que realizam exames de seleção por meio de avaliações presenciais. Definir uma distribuição adequada considerando critérios como distância, custo e ocupação é uma tarefa desafiadora. Este trabalho trata a tarefa em questão como um problema combinatório de casamento estável e propõe um novo algoritmo de otimização (O2MSM) para a distribuição automática de candidatos a locais de teste. O O2MSM visa encontrar uma correspondência estável entre candidatos e locais de prova, minimizando a distância entre eles e o número de locais de prova e maximizando a taxa de ocupação desses locais. Os resultados mostraram que o O2MSM superou a abordagem baseline, sendo mais eficiente, realizando a distribuição dos candidatos em segundos, e mais eficaz, reduzindo ao máximo o número de locais de prova, vagas e distância dos candidatos.
{"title":"A Novel One-to-Many Matching Method for the Assignment Problem: An ENEM Case Study","authors":"Giuseppe F. Neto, Pericles Miranda, R. F. Mello, André C. A. Nascimento","doi":"10.5753/eniac.2022.227548","DOIUrl":"https://doi.org/10.5753/eniac.2022.227548","url":null,"abstract":"A distribuição de candidatos em locais de provas é um problema logístico relevante e afeta diversos países, inclusive o Brasil, que realizam exames de seleção por meio de avaliações presenciais. Definir uma distribuição adequada considerando critérios como distância, custo e ocupação é uma tarefa desafiadora. Este trabalho trata a tarefa em questão como um problema combinatório de casamento estável e propõe um novo algoritmo de otimização (O2MSM) para a distribuição automática de candidatos a locais de teste. O O2MSM visa encontrar uma correspondência estável entre candidatos e locais de prova, minimizando a distância entre eles e o número de locais de prova e maximizando a taxa de ocupação desses locais. Os resultados mostraram que o O2MSM superou a abordagem baseline, sendo mais eficiente, realizando a distribuição dos candidatos em segundos, e mais eficaz, reduzindo ao máximo o número de locais de prova, vagas e distância dos candidatos.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122952510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227217
Larissa F. S. Britto, Luis A. S. Pessoa, Silvania C. C. Agostinho
O Cruzamento de Domínios tem se tornado uma abordagem comum para lidar com a escassez de dados rotulados na Análise de Sentimentos (AS). No entanto, a dependência de domínio da AS e as particularidades associadas a cada domínio podem impactar, negativamente, o desempenho dos modelos de classificação. Neste trabalho, avaliamos a capacidade de generalização do modelo BERT na tarefa de Classificação de Polaridade com Cruzamento de Domínios em Português. Para fins de comparação, classificadores tradicionais de Aprendizagem de Máquina e métodos para extração de características são analisados. O BERT apresentou resultados promissores mesmo com a mudança de domínio, chegando a alcançar 92% de acurácia.
{"title":"Cross-Domain Sentiment Analysis in Portuguese using BERT","authors":"Larissa F. S. Britto, Luis A. S. Pessoa, Silvania C. C. Agostinho","doi":"10.5753/eniac.2022.227217","DOIUrl":"https://doi.org/10.5753/eniac.2022.227217","url":null,"abstract":"O Cruzamento de Domínios tem se tornado uma abordagem comum para lidar com a escassez de dados rotulados na Análise de Sentimentos (AS). No entanto, a dependência de domínio da AS e as particularidades associadas a cada domínio podem impactar, negativamente, o desempenho dos modelos de classificação. Neste trabalho, avaliamos a capacidade de generalização do modelo BERT na tarefa de Classificação de Polaridade com Cruzamento de Domínios em Português. Para fins de comparação, classificadores tradicionais de Aprendizagem de Máquina e métodos para extração de características são analisados. O BERT apresentou resultados promissores mesmo com a mudança de domínio, chegando a alcançar 92% de acurácia.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126990239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227328
Diego S. Sarafim, K. V. Delgado, D. Cordeiro
JavaScript has become one of the most widely used programming languages. JavaScript is a dynamic, interpreted, and weakly-typed scripting language especially suited for the development of web applications. While these characteristics allow the language to offer high levels of flexibility, they also can make JavaScript code more challenging to write, maintain and evolve. One of the risks that JavaScript and other programming languages are prone to is the presence of code smells. Code smells result from poor programming choices during source code development that negatively influence source code comprehension and maintainability in the long term. This work reports the result of an approach that uses the Random Forest algorithm to detect a set of 11 code smells based on software metrics extracted from JavaScript source code. It also reports the construction of two datasets, one for code smells that affect functions/methods, and another for code smells related to classes, both containing at least 200 labeled positive instances of each code smell and both extracted from a set of 25 open-source JavaScript projects.
{"title":"Random Forest for Code Smell Detection in JavaScript","authors":"Diego S. Sarafim, K. V. Delgado, D. Cordeiro","doi":"10.5753/eniac.2022.227328","DOIUrl":"https://doi.org/10.5753/eniac.2022.227328","url":null,"abstract":"JavaScript has become one of the most widely used programming languages. JavaScript is a dynamic, interpreted, and weakly-typed scripting language especially suited for the development of web applications. While these characteristics allow the language to offer high levels of flexibility, they also can make JavaScript code more challenging to write, maintain and evolve. One of the risks that JavaScript and other programming languages are prone to is the presence of code smells. Code smells result from poor programming choices during source code development that negatively influence source code comprehension and maintainability in the long term. This work reports the result of an approach that uses the Random Forest algorithm to detect a set of 11 code smells based on software metrics extracted from JavaScript source code. It also reports the construction of two datasets, one for code smells that affect functions/methods, and another for code smells related to classes, both containing at least 200 labeled positive instances of each code smell and both extracted from a set of 25 open-source JavaScript projects.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123401504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227396
Rodolfo Seidel, Hilário Seibel Júnior, K. S. Komati
Devido à identificação manual de defeitos têxteis ainda nos dias atuais, é necessário encontrar meios de detectar defeitos de forma automatizada e eficiente. Para isso, este trabalho se propõe a aplicar o modelo YOLOv5 na base de dados AITEX, usando a abordagem de detecção de objetos para localizar e identificar defeitos, avaliando diferentes técnicas de anotação de objetos e data augmentation. Com os resultados obtidos, concluiu-se que o YOLOv5 adaptou-se muito bem a outro contexto com objetos distintos do prétreinamento, as anotações com Bounding Boxes permitiram maior aprendizado e reconhecimento dos defeitos, mesmo com diferentes formas e tamanhos, e por fim, a combinação de data augmentation potencializam seu desempenho.
{"title":"Textile defect detection using YOLOv5 on AITEX Dataset","authors":"Rodolfo Seidel, Hilário Seibel Júnior, K. S. Komati","doi":"10.5753/eniac.2022.227396","DOIUrl":"https://doi.org/10.5753/eniac.2022.227396","url":null,"abstract":"Devido à identificação manual de defeitos têxteis ainda nos dias atuais, é necessário encontrar meios de detectar defeitos de forma automatizada e eficiente. Para isso, este trabalho se propõe a aplicar o modelo YOLOv5 na base de dados AITEX, usando a abordagem de detecção de objetos para localizar e identificar defeitos, avaliando diferentes técnicas de anotação de objetos e data augmentation. Com os resultados obtidos, concluiu-se que o YOLOv5 adaptou-se muito bem a outro contexto com objetos distintos do prétreinamento, as anotações com Bounding Boxes permitiram maior aprendizado e reconhecimento dos defeitos, mesmo com diferentes formas e tamanhos, e por fim, a combinação de data augmentation potencializam seu desempenho.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114723903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227630
Marco Aurelio Bastos Souza, Edson Eduardo Borges da Silva, João Pedro M. Tarrega, R. Tinós, A. Costa
The light-dark box is a widely used test for the investigation of animal behavior commonly used to identify and study anxious-like behavioral patterns in rodents. We propose a neuroevolution model for virtual rats in a simulated light-dark box. The virtual rat is controlled by an artificial neural network (ANN) optimized by a genetic algorithm (GA). The fitness function is given by a weighed sum of two terms (punishment and reward). By changing the weight of the punishment term, we are able to simulate the effects of anxiolytic/anxiogenic drugs on rats. We also propose using GAs to optimize the number of the ANN hidden neurons and sensors for the virtual rat. According to the experiments, the best results are obtained by ANNs combining both luminosity and wall sensors.
{"title":"Simulation of Rat Behavior in a Light-Dark Box via Neuroevolution","authors":"Marco Aurelio Bastos Souza, Edson Eduardo Borges da Silva, João Pedro M. Tarrega, R. Tinós, A. Costa","doi":"10.5753/eniac.2022.227630","DOIUrl":"https://doi.org/10.5753/eniac.2022.227630","url":null,"abstract":"The light-dark box is a widely used test for the investigation of animal behavior commonly used to identify and study anxious-like behavioral patterns in rodents. We propose a neuroevolution model for virtual rats in a simulated light-dark box. The virtual rat is controlled by an artificial neural network (ANN) optimized by a genetic algorithm (GA). The fitness function is given by a weighed sum of two terms (punishment and reward). By changing the weight of the punishment term, we are able to simulate the effects of anxiolytic/anxiogenic drugs on rats. We also propose using GAs to optimize the number of the ANN hidden neurons and sensors for the virtual rat. According to the experiments, the best results are obtained by ANNs combining both luminosity and wall sensors.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"4 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133238140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.5753/eniac.2022.227641
J. E. H. D. Silva, L. N. S. Prachedes, H. Bernardino, J. Camata, I. L. Oliveira
Evolutionary techniques have been used in the design and optimization of combinational logic circuits. This procedure is called evolvable hardware and Cartesian Genetic Programming (CGP) is the evolutionary technique with the best performance in this context. Despite the good results obtained by CGP techniques, its search procedure usually evolves a single candidate solution by an evolution strategy and this approach tends to be trapped in local optima. On the other hand, clonal selection techniques in general, and CLONALG in particular, were designed to avoid converging to a low-quality local optimum. Thus, we propose here using the representation of CGP with the search procedure of a Clonal Selection Algorithm to minimize the number of transistors of combinational logic circuits. Furthermore, a parameter sensitivity analysis is performed. The results are assessed considering a benchmark from the literature and showed a reduction in the number of transistors when compared to the baseline ESPRESSO.
{"title":"On the Use of Clonal Selection Principle in Cartesian Genetic Programming for Designing Combinational Logic Circuits","authors":"J. E. H. D. Silva, L. N. S. Prachedes, H. Bernardino, J. Camata, I. L. Oliveira","doi":"10.5753/eniac.2022.227641","DOIUrl":"https://doi.org/10.5753/eniac.2022.227641","url":null,"abstract":"Evolutionary techniques have been used in the design and optimization of combinational logic circuits. This procedure is called evolvable hardware and Cartesian Genetic Programming (CGP) is the evolutionary technique with the best performance in this context. Despite the good results obtained by CGP techniques, its search procedure usually evolves a single candidate solution by an evolution strategy and this approach tends to be trapped in local optima. On the other hand, clonal selection techniques in general, and CLONALG in particular, were designed to avoid converging to a low-quality local optimum. Thus, we propose here using the representation of CGP with the search procedure of a Clonal Selection Algorithm to minimize the number of transistors of combinational logic circuits. Furthermore, a parameter sensitivity analysis is performed. The results are assessed considering a benchmark from the literature and showed a reduction in the number of transistors when compared to the baseline ESPRESSO.","PeriodicalId":165095,"journal":{"name":"Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022)","volume":"359 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120895734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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