Pub Date : 2022-10-24DOI: 10.4018/978-1-4666-4876-0.chcrp
{"title":"Copyright page","authors":"","doi":"10.4018/978-1-4666-4876-0.chcrp","DOIUrl":"https://doi.org/10.4018/978-1-4666-4876-0.chcrp","url":null,"abstract":"","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128685137","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949489
M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni
In the framework of multi-objective evolutionary fuzzy systems applied to regression problems, we propose to concurrently exploit a two-level rule selection (2LRS) and an appropriate learning of the membership function (MF) parameters to generate a set of Mamdani fuzzy rule-based systems with different trade-offs between accuracy and RB complexity. The 2LRS aims to select a reduced number of rules from a previously generated rule base and a reduced number of conditions for each selected rule. The learning adapts the cores of the MFs maintaining the partitions strong. The proposed approach has been experimented on two real world regression problems and the results have been compared with those obtained by applying the same multi-objective evolutionary algorithm for learning concurrently rules and MF parameters. We show that our approach achieves the best trade-offs between interpretability and accuracy.
{"title":"Multi-objective evolutionary generation of Mamdani fuzzy rule-based systems based on rule and condition selection","authors":"M. Antonelli, P. Ducange, B. Lazzerini, F. Marcelloni","doi":"10.1109/GEFS.2011.5949489","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949489","url":null,"abstract":"In the framework of multi-objective evolutionary fuzzy systems applied to regression problems, we propose to concurrently exploit a two-level rule selection (2LRS) and an appropriate learning of the membership function (MF) parameters to generate a set of Mamdani fuzzy rule-based systems with different trade-offs between accuracy and RB complexity. The 2LRS aims to select a reduced number of rules from a previously generated rule base and a reduced number of conditions for each selected rule. The learning adapts the cores of the MFs maintaining the partitions strong. The proposed approach has been experimented on two real world regression problems and the results have been compared with those obtained by applying the same multi-objective evolutionary algorithm for learning concurrently rules and MF parameters. We show that our approach achieves the best trade-offs between interpretability and accuracy.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131879655","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949502
Raffaele Cannone, J. M. Alonso, L. Magdalena
Although recently there has been many papers dealing with how to characterize and assess interpretability, there is still a lot of work to be done. Interpretability assessment is usually addressed by evaluating the complexity and/or readability of fuzzy rule-based systems. However, comprehensibility is usually not taken into account because it implies more cognitive aspects which are difficult to formalize and to deal with. In this work we show the importance of considering not only readability but also comprehensibility during the design process of fuzzy systems. We introduce the use of a novel index for evaluating comprehensibility in the context of a three-objective evolutionary framework for designing highly interpretable fuzzy rule-based classifiers. It is named as logical view index (LVI) and it is based on a semantic cointension approach. The proposed evolutionary algorithm consists of embedding the HILK (Highly Interpretable Linguistic Knowledge) fuzzy modeling methodology into the classical NSGA-II with the aim of maximizing accuracy, readability, and comprehensibility of the generated fuzzy rule-based classifiers. Our proposal is tested in the well-known PIMA benchmark problem which corresponds to a medical diagnosis problem where interpretability is a strong requirement.
{"title":"Multi-objective design of highly interpretable fuzzy rule-based classifiers with semantic cointension","authors":"Raffaele Cannone, J. M. Alonso, L. Magdalena","doi":"10.1109/GEFS.2011.5949502","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949502","url":null,"abstract":"Although recently there has been many papers dealing with how to characterize and assess interpretability, there is still a lot of work to be done. Interpretability assessment is usually addressed by evaluating the complexity and/or readability of fuzzy rule-based systems. However, comprehensibility is usually not taken into account because it implies more cognitive aspects which are difficult to formalize and to deal with. In this work we show the importance of considering not only readability but also comprehensibility during the design process of fuzzy systems. We introduce the use of a novel index for evaluating comprehensibility in the context of a three-objective evolutionary framework for designing highly interpretable fuzzy rule-based classifiers. It is named as logical view index (LVI) and it is based on a semantic cointension approach. The proposed evolutionary algorithm consists of embedding the HILK (Highly Interpretable Linguistic Knowledge) fuzzy modeling methodology into the classical NSGA-II with the aim of maximizing accuracy, readability, and comprehensibility of the generated fuzzy rule-based classifiers. Our proposal is tested in the well-known PIMA benchmark problem which corresponds to a medical diagnosis problem where interpretability is a strong requirement.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176042","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949492
S. Njah, H. Bezine, A. Alimi
In this paper, we present a fuzzy genetic system for handwriting segmentation via perceptual codes based on assumptions of PerTOHS theory, which correspond to: handwriting is a form and a sequence of perceptual codes. Studying handwriting, we perceive the existence of elementary and global perceptual codes. Gathering the elementary ones in a choice of constraints we obtain global ones, and to obtain different forms of handwriting, we proceed by a perceptual organization of them. So, we present a new fuzzy genetic system to improve handwriting segmentation via perceptual codes. This system is based on the Beta-elliptic model, uses the fuzzy set theory to detect the elementary perceptual codes (EPCs) and the genetic algorithms for the global perceptual ones (GPCs). To validate our new system, we use ADAB database. The obtained results show successful representations of handwritten script via perceptual codes.
{"title":"A fuzzy genetic system for segmentation of on-line handwriting: Application to ADAB database","authors":"S. Njah, H. Bezine, A. Alimi","doi":"10.1109/GEFS.2011.5949492","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949492","url":null,"abstract":"In this paper, we present a fuzzy genetic system for handwriting segmentation via perceptual codes based on assumptions of PerTOHS theory, which correspond to: handwriting is a form and a sequence of perceptual codes. Studying handwriting, we perceive the existence of elementary and global perceptual codes. Gathering the elementary ones in a choice of constraints we obtain global ones, and to obtain different forms of handwriting, we proceed by a perceptual organization of them. So, we present a new fuzzy genetic system to improve handwriting segmentation via perceptual codes. This system is based on the Beta-elliptic model, uses the fuzzy set theory to detect the elementary perceptual codes (EPCs) and the genetic algorithms for the global perceptual ones (GPCs). To validate our new system, we use ADAB database. The obtained results show successful representations of handwritten script via perceptual codes.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114383919","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949495
M. Cococcioni, R. Grasso, M. Rixen
The most popular inference method in Takagi-Sugeno (TS) fuzzy systems is the weighted averaging (WA), whereas the most investigated inference method in fuzzy rule-based classifier is probably the winner-takes-all (WTA). This paper first shows the time complexities associated with WA and WTA inference methods in Takagi-Sugeno fuzzy rule-based systems, also highlighting the strengths and the weaknesses of both approaches. Then it argues that using a hybrid of the two inference methods, namely the WTA during identification and the WA during the evaluation, allows advantaging of the strong points of the two methods, without inheriting most of their weakness. In particular, the hybrid formulation has a nice property which can be even mandatory in particular applications: it both guarantees that the TS system is continuous (provided that infinite support membership functions are used) and that it performs an approximate reasoning, by combining the conclusions of more than one rule. The interesting features of the hybrid method are demonstrated on a multiobjective genetic rule learning framework used for regression.
{"title":"A hybrid continuity preserving inference strategy to speed up Takagi-Sugeno multiobjective genetic fuzzy systems","authors":"M. Cococcioni, R. Grasso, M. Rixen","doi":"10.1109/GEFS.2011.5949495","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949495","url":null,"abstract":"The most popular inference method in Takagi-Sugeno (TS) fuzzy systems is the weighted averaging (WA), whereas the most investigated inference method in fuzzy rule-based classifier is probably the winner-takes-all (WTA). This paper first shows the time complexities associated with WA and WTA inference methods in Takagi-Sugeno fuzzy rule-based systems, also highlighting the strengths and the weaknesses of both approaches. Then it argues that using a hybrid of the two inference methods, namely the WTA during identification and the WA during the evaluation, allows advantaging of the strong points of the two methods, without inheriting most of their weakness. In particular, the hybrid formulation has a nice property which can be even mandatory in particular applications: it both guarantees that the TS system is continuous (provided that infinite support membership functions are used) and that it performs an approximate reasoning, by combining the conclusions of more than one rule. The interesting features of the hybrid method are demonstrated on a multiobjective genetic rule learning framework used for regression.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131775044","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949496
Tracy Pik Yin Mok
Effective and accurate production planning is essential for garment manufacturers to survive in today's competitive apparel industry. Varying customer demands, shorter lifecycles and changing fashion trends are amongst the factors that make accurate production planning important. Manufacturers strive to fulfil requirements such as on-time completion, short production lead time and effective allocation of job order to specific production lines. However, effective production planning is difficult to achieve because the apparel manufacturing environment is fuzzy and dynamic. This paper suggests the use of intelligent production planning algorithms, based on fuzzy set theory, genetic algorithms (GA) and multi-objective genetic algorithms (MOGA), to achieve optimal solutions for apparel production planning.
{"title":"Intelligent apparel production planning for optimizing manual operations using fuzzy set theory and evolutionary algorithms","authors":"Tracy Pik Yin Mok","doi":"10.1109/GEFS.2011.5949496","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949496","url":null,"abstract":"Effective and accurate production planning is essential for garment manufacturers to survive in today's competitive apparel industry. Varying customer demands, shorter lifecycles and changing fashion trends are amongst the factors that make accurate production planning important. Manufacturers strive to fulfil requirements such as on-time completion, short production lead time and effective allocation of job order to specific production lines. However, effective production planning is difficult to achieve because the apparel manufacturing environment is fuzzy and dynamic. This paper suggests the use of intelligent production planning algorithms, based on fuzzy set theory, genetic algorithms (GA) and multi-objective genetic algorithms (MOGA), to achieve optimal solutions for apparel production planning.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131094900","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949499
Michel González, J. Casillas, Carlos Morell
Multi-objective genetic learning of Fuzzy Rule-Based Systems (FRBSs) is a very prolific investigation trend. The use of more optimization objectives to cover more aspects of the fuzzy model is very convenient, but also leads to a many-objective problem that is intractable with classical algorithms. This paper proposes three distinct categories of interpretability measures that can be used for optimization. Moreover, it introduces a new interpretability measure for fuzzy tuning. The proposed metric is implemented into a state-of-the-art algorithm that includes many-objectives techniques which allow the use of more objectives without substantial degradation. The new algorithm is tested in a set of real-world regression problems with successful results.
{"title":"Dealing with three uncorrelated criteria by many-objective genetic fuzzy systems","authors":"Michel González, J. Casillas, Carlos Morell","doi":"10.1109/GEFS.2011.5949499","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949499","url":null,"abstract":"Multi-objective genetic learning of Fuzzy Rule-Based Systems (FRBSs) is a very prolific investigation trend. The use of more optimization objectives to cover more aspects of the fuzzy model is very convenient, but also leads to a many-objective problem that is intractable with classical algorithms. This paper proposes three distinct categories of interpretability measures that can be used for optimization. Moreover, it introduces a new interpretability measure for fuzzy tuning. The proposed metric is implemented into a state-of-the-art algorithm that includes many-objectives techniques which allow the use of more objectives without substantial degradation. The new algorithm is tested in a set of real-world regression problems with successful results.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121639100","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949497
Stephen G. Matthews, M. Gongora, A. Hopgood
We present a novel method for mining itemsets that are both quantitative and temporal, for association rule mining, using multi-objective evolutionary search and optimisation. This method successfully identifies temporal itemsets that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. Current approaches preprocess data which can often lead to a loss of information. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy itemsets and the approach of using a multi-objective evolutionary algorithm. This preliminary work presents the problem, a novel approach and promising results that will lead to future work. Results show the ability of NSGA-II to evolve target itemsets that have been augmented into synthetic datasets. Itemsets with different levels of support have been augmented to demonstrate this approach with varying difficulties.
{"title":"Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm","authors":"Stephen G. Matthews, M. Gongora, A. Hopgood","doi":"10.1109/GEFS.2011.5949497","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949497","url":null,"abstract":"We present a novel method for mining itemsets that are both quantitative and temporal, for association rule mining, using multi-objective evolutionary search and optimisation. This method successfully identifies temporal itemsets that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. Current approaches preprocess data which can often lead to a loss of information. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy itemsets and the approach of using a multi-objective evolutionary algorithm. This preliminary work presents the problem, a novel approach and promising results that will lead to future work. Results show the ability of NSGA-II to evolve target itemsets that have been augmented into synthetic datasets. Itemsets with different levels of support have been augmented to demonstrate this approach with varying difficulties.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"3 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133576482","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949494
M. J. Gacto, R. Alcalá, F. Herrera
In this work, we present an advanced Multi-Objective Genetic Algorithm for obtaining more compact fuzzy logic controllers as the way to find the best combination of rules, thus improving the system performance in a problem to control a Heating, Ventilating, and Air Conditioning system. To this end, two objectives have been considered, maximizing the performance of the system (involving energy performance, stability and indoor comfort requirements) and minimizing the number of rules obtained (for finding the most cooperative/accurate rule subset).
{"title":"Evolutionary Multi-Objective Algorithm to effectively improve the performance of the classic tuning of fuzzy logic controllers for a heating, ventilating and Air Conditioning system","authors":"M. J. Gacto, R. Alcalá, F. Herrera","doi":"10.1109/GEFS.2011.5949494","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949494","url":null,"abstract":"In this work, we present an advanced Multi-Objective Genetic Algorithm for obtaining more compact fuzzy logic controllers as the way to find the best combination of rules, thus improving the system performance in a problem to control a Heating, Ventilating, and Air Conditioning system. To this end, two objectives have been considered, maximizing the performance of the system (involving energy performance, stability and indoor comfort requirements) and minimizing the number of rules obtained (for finding the most cooperative/accurate rule subset).","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124231742","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 : 2011-04-11DOI: 10.1109/GEFS.2011.5949493
A. Alvarez-Alvarez, G. Triviño, O. Cordón
Body posture recognition is a very important issue as a basis for the detection of user's behavior. In this paper, we propose the use of a genetic fuzzy finite state machine for this real-world application.
{"title":"Body posture recognition by means of a genetic fuzzy finite state machine","authors":"A. Alvarez-Alvarez, G. Triviño, O. Cordón","doi":"10.1109/GEFS.2011.5949493","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949493","url":null,"abstract":"Body posture recognition is a very important issue as a basis for the detection of user's behavior. In this paper, we propose the use of a genetic fuzzy finite state machine for this real-world application.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115917286","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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