Pub Date : 2016-12-01DOI: 10.1109/SSCI.2016.7850183
B. Mocialov, P. A. Vargas, M. Couceiro
This paper presents preliminary investigations on the evolution of indirect communication between two agents. In the future, behaviours of robots in the RoboCup1 competition should resemble the behaviours of the human players. One common trait of this behaviour is the indirect communication. Within the human-robot-interaction, indirect communication can either be the principal or supporting method for information exchange. This paper summarises previous work on the topic and presents the design of a self-organised system for gesture recognition. Although, preliminary results show that the proposed system requires further feature extraction improvements and evaluations on various public datasets, the system is capable of performing classification of gestures. Further research is required to fully investigate potential extensions to the system that would be able to support real indirect communication in human-robot interaction scenarios.
{"title":"Towards the evolution of indirect communication for social robots","authors":"B. Mocialov, P. A. Vargas, M. Couceiro","doi":"10.1109/SSCI.2016.7850183","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850183","url":null,"abstract":"This paper presents preliminary investigations on the evolution of indirect communication between two agents. In the future, behaviours of robots in the RoboCup1 competition should resemble the behaviours of the human players. One common trait of this behaviour is the indirect communication. Within the human-robot-interaction, indirect communication can either be the principal or supporting method for information exchange. This paper summarises previous work on the topic and presents the design of a self-organised system for gesture recognition. Although, preliminary results show that the proposed system requires further feature extraction improvements and evaluations on various public datasets, the system is capable of performing classification of gestures. Further research is required to fully investigate potential extensions to the system that would be able to support real indirect communication in human-robot interaction scenarios.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122917138","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7849908
Neal Wagner, C. Sahin, M. Winterrose, J. Riordan, Jaime Peña, D. Hanson, W. Streilein
Network segmentation is a security measure that partitions a network into sections or segments to restrict the movement of a cyber attacker and make it difficult for her to gain access to valuable network resources. This threat-mitigating practice has been recommended by several information security agencies. While it is clear that segmentation is a critical defensive mitigation against cyber threats, it is not clear how to properly apply it. Current standards only offer vague guidance on how to apply segmentation and, thus, practitioners must rely on judgment. This paper examines the problem from a decision support perspective: that is, how can an appropriate segmentation for a given network environment be selected? We propose a novel method for supporting such a decision that utilizes an approach based on heuristic search and agent-based simulation. We have implemented a first prototype of our method and illustrate its use via a case study on a representative network environment.
{"title":"Towards automated cyber decision support: A case study on network segmentation for security","authors":"Neal Wagner, C. Sahin, M. Winterrose, J. Riordan, Jaime Peña, D. Hanson, W. Streilein","doi":"10.1109/SSCI.2016.7849908","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849908","url":null,"abstract":"Network segmentation is a security measure that partitions a network into sections or segments to restrict the movement of a cyber attacker and make it difficult for her to gain access to valuable network resources. This threat-mitigating practice has been recommended by several information security agencies. While it is clear that segmentation is a critical defensive mitigation against cyber threats, it is not clear how to properly apply it. Current standards only offer vague guidance on how to apply segmentation and, thus, practitioners must rely on judgment. This paper examines the problem from a decision support perspective: that is, how can an appropriate segmentation for a given network environment be selected? We propose a novel method for supporting such a decision that utilizes an approach based on heuristic search and agent-based simulation. We have implemented a first prototype of our method and illustrate its use via a case study on a representative network environment.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756587","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7849954
Abeer Alzubaidi, David J. Brown, G. Cosma, A. Pockley
Reducing the number of features whilst maintaining an acceptable classification accuracy is a fundamental step in the process of constructing cancer predictive models. In this work, we introduce a novel hybrid (MI-LDA) feature selection approach for the diagnosis of ovarian cancer. This hybrid approach is embedded within a global optimization framework and offers a promising improvement on feature selection and classification accuracy processes. Global Mutual Information (MI) based feature selection optimizes the search process of finding best feature subsets in order to select the highly correlated predictors for ovarian cancer diagnosis. The maximal discriminative cancer predictors are then passed to a Linear Discriminant Analysis (LDA) classifier, and a Genetic Algorithm (GA) is applied to optimise the search process with respect to the estimated error rate of the LDA classifier (MI-LDA). Experiments were performed using an ovarian cancer dataset obtained from the FDA-NCI Clinical Proteomics Program Databank. The performance of the hybrid feature selection approach was evaluated using the Support Vector Machine (SVM) classifier and the LDA classifier. A comparison of the results revealed that the proposed (MI-LDA)-LDA model outperformed the (MI-LDA)-SVM model on selecting the maximal discriminative feature subset and achieved the highest predictive accuracy. The proposed system can therefore be used as an efficient tool for finding predictors and patterns in serum (blood)-derived proteomic data for the detection of ovarian cancer.
{"title":"A new hybrid global optimization approach for selecting clinical and biological features that are relevant to the effective diagnosis of ovarian cancer","authors":"Abeer Alzubaidi, David J. Brown, G. Cosma, A. Pockley","doi":"10.1109/SSCI.2016.7849954","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849954","url":null,"abstract":"Reducing the number of features whilst maintaining an acceptable classification accuracy is a fundamental step in the process of constructing cancer predictive models. In this work, we introduce a novel hybrid (MI-LDA) feature selection approach for the diagnosis of ovarian cancer. This hybrid approach is embedded within a global optimization framework and offers a promising improvement on feature selection and classification accuracy processes. Global Mutual Information (MI) based feature selection optimizes the search process of finding best feature subsets in order to select the highly correlated predictors for ovarian cancer diagnosis. The maximal discriminative cancer predictors are then passed to a Linear Discriminant Analysis (LDA) classifier, and a Genetic Algorithm (GA) is applied to optimise the search process with respect to the estimated error rate of the LDA classifier (MI-LDA). Experiments were performed using an ovarian cancer dataset obtained from the FDA-NCI Clinical Proteomics Program Databank. The performance of the hybrid feature selection approach was evaluated using the Support Vector Machine (SVM) classifier and the LDA classifier. A comparison of the results revealed that the proposed (MI-LDA)-LDA model outperformed the (MI-LDA)-SVM model on selecting the maximal discriminative feature subset and achieved the highest predictive accuracy. The proposed system can therefore be used as an efficient tool for finding predictors and patterns in serum (blood)-derived proteomic data for the detection of ovarian cancer.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127008676","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7849866
Pierfrancesco Cervellini, A. G. Menezes, Vijay Mago
The search for Trendsetters in social networks turned to be a complex research topic that has gained much attention. The work here presented uses big data analytics to find who better spreads the word in a social network and is innovative in their choices. The analysis on the Yelp platform can be divided in three parts: first, we justify the use of Tips frequency as a variable to profile business popularity. Second we analyze Tips frequency to select businesses that fit a growing popularity profile. And third we graph mine the sociographs generated by the users that interacted with each selected business. Top nodes are ranked by using Indegree, Eigenvector centrality, Pagerank and a Trendsetter algorithms, and we compare the relative performance of each algorithm. Our findings indicate that the Trendsetter ranking algorithm is the most performant at finding nodes that best reflect the Trendsetter properties.
{"title":"Finding Trendsetters on Yelp Dataset","authors":"Pierfrancesco Cervellini, A. G. Menezes, Vijay Mago","doi":"10.1109/SSCI.2016.7849866","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849866","url":null,"abstract":"The search for Trendsetters in social networks turned to be a complex research topic that has gained much attention. The work here presented uses big data analytics to find who better spreads the word in a social network and is innovative in their choices. The analysis on the Yelp platform can be divided in three parts: first, we justify the use of Tips frequency as a variable to profile business popularity. Second we analyze Tips frequency to select businesses that fit a growing popularity profile. And third we graph mine the sociographs generated by the users that interacted with each selected business. Top nodes are ranked by using Indegree, Eigenvector centrality, Pagerank and a Trendsetter algorithms, and we compare the relative performance of each algorithm. Our findings indicate that the Trendsetter ranking algorithm is the most performant at finding nodes that best reflect the Trendsetter properties.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116511705","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850015
Ezequiel Avilés-Ochoa, Ernesto León-Castro, J. M. Lindahl, A. M. G. Lafuente
This paper presents the results of using experton, forgotten effects and heavy moving averages operators in three traditional models based purchasing power parity (PPP) model to forecast exchange rate. Therefore, the use of these methods is to improve the forecast error under scenarios of volatility and uncertainty, such as the financial markets and more precise in exchange rate. The heavy ordered weighted moving average weighted average (HOWMAWA) operator is introduced. This new operator includes the weighted average in the usual heavy ordered weighted moving average (HOWMA) operator, considering a degree of importance for each concept that includes the operator. The use of experton and forgotten effects methodology represents the information of the experts in the field and with that information were obtained hidden variables or second degree relations. The results show that the inclusion of the forgotten effects and heavy moving average operators improve our results and reduce the forecast error.
{"title":"Forgotten effects and heavy moving averages in exchange rate forecasting","authors":"Ezequiel Avilés-Ochoa, Ernesto León-Castro, J. M. Lindahl, A. M. G. Lafuente","doi":"10.1109/SSCI.2016.7850015","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850015","url":null,"abstract":"This paper presents the results of using experton, forgotten effects and heavy moving averages operators in three traditional models based purchasing power parity (PPP) model to forecast exchange rate. Therefore, the use of these methods is to improve the forecast error under scenarios of volatility and uncertainty, such as the financial markets and more precise in exchange rate. The heavy ordered weighted moving average weighted average (HOWMAWA) operator is introduced. This new operator includes the weighted average in the usual heavy ordered weighted moving average (HOWMA) operator, considering a degree of importance for each concept that includes the operator. The use of experton and forgotten effects methodology represents the information of the experts in the field and with that information were obtained hidden variables or second degree relations. The results show that the inclusion of the forgotten effects and heavy moving average operators improve our results and reduce the forecast error.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127826735","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850078
Duc C. Le, A. N. Zincir-Heywood, M. Heywood
Botnets represent one of the most destructive cybersecurity threats. Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection. In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances. In this paper, we investigate the capability of employing the Self-Organizing Map (SOM), an unsupervised learning technique as a data analytics system. In doing so, our aim is to understand how far such an approach could be pushed to analyze unknown traffic to detect botnets. To this end, we employed three different unsupervised training schemes using publicly available botnet data sets. Our results show that SOMs possess high potential as a data analytics tool on unknown traffic. They can identify the botnet and normal flows with high confidence approximately 99% of the time on the data sets employed in this work.
{"title":"Data analytics on network traffic flows for botnet behaviour detection","authors":"Duc C. Le, A. N. Zincir-Heywood, M. Heywood","doi":"10.1109/SSCI.2016.7850078","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850078","url":null,"abstract":"Botnets represent one of the most destructive cybersecurity threats. Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection. In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances. In this paper, we investigate the capability of employing the Self-Organizing Map (SOM), an unsupervised learning technique as a data analytics system. In doing so, our aim is to understand how far such an approach could be pushed to analyze unknown traffic to detect botnets. To this end, we employed three different unsupervised training schemes using publicly available botnet data sets. Our results show that SOMs possess high potential as a data analytics tool on unknown traffic. They can identify the botnet and normal flows with high confidence approximately 99% of the time on the data sets employed in this work.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133354505","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7849938
Willem S. van Heerden, A. Engelbrecht
Self-Organizing Maps (SOMs) are unsupervised neural networks that build data models. Neuron labeling attaches descriptive textual labels to the neurons making up a SOM, and is an important component of SOM-based exploratory data analysis (EDA) and data mining (DM). Several neuron labeling approaches tend to leave some neurons unlabeled. The interaction between unlabeled neurons and SOM model accuracy affect the choice of labeling algorithm for SOM-based EDA and DM, but has not been previously investigated. This paper applies the widely used example-centric neuron labeling algorithm to several classification problems, and empirically investigates the relationship between the percentage of neurons left unlabeled and classification accuracy. Practical recommendations are also presented, which address the treatment of unlabeled neurons and the selection of an appropriate neuron labeling algorithm.
{"title":"An investigation into the effect of unlabeled neurons on Self-Organizing Maps","authors":"Willem S. van Heerden, A. Engelbrecht","doi":"10.1109/SSCI.2016.7849938","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849938","url":null,"abstract":"Self-Organizing Maps (SOMs) are unsupervised neural networks that build data models. Neuron labeling attaches descriptive textual labels to the neurons making up a SOM, and is an important component of SOM-based exploratory data analysis (EDA) and data mining (DM). Several neuron labeling approaches tend to leave some neurons unlabeled. The interaction between unlabeled neurons and SOM model accuracy affect the choice of labeling algorithm for SOM-based EDA and DM, but has not been previously investigated. This paper applies the widely used example-centric neuron labeling algorithm to several classification problems, and empirically investigates the relationship between the percentage of neurons left unlabeled and classification accuracy. Practical recommendations are also presented, which address the treatment of unlabeled neurons and the selection of an appropriate neuron labeling algorithm.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133455668","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850265
C. Cleghorn, A. Engelbrecht
There exists a wealth of theoretical analysis on particle swarm optimization (PSO), specifically the conditions needed for stable particle behavior are well studied. This paper investigates the effect that the stability of the particle has on the PSO's actually ability to optimize. It is shown empirically that a majority of PSO parameters that are theoretically unstable perform worse than a trivial random search across 28 objective functions, and across various dimensionalities. It is also noted that there exists a number of parameter configurations just outside the stable-2 region which did not exhibit poor performance, implying that a minor violation of the conditions for order-2 stability is still acceptable in terms of overall performance of the PSO.
{"title":"Particle swarm optimizer: The impact of unstable particles on performance","authors":"C. Cleghorn, A. Engelbrecht","doi":"10.1109/SSCI.2016.7850265","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850265","url":null,"abstract":"There exists a wealth of theoretical analysis on particle swarm optimization (PSO), specifically the conditions needed for stable particle behavior are well studied. This paper investigates the effect that the stability of the particle has on the PSO's actually ability to optimize. It is shown empirically that a majority of PSO parameters that are theoretically unstable perform worse than a trivial random search across 28 objective functions, and across various dimensionalities. It is also noted that there exists a number of parameter configurations just outside the stable-2 region which did not exhibit poor performance, implying that a minor violation of the conditions for order-2 stability is still acceptable in terms of overall performance of the PSO.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131869988","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7849936
G. Acampora, G. Tortora, A. Vitiello
The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and good performance. However, in spite of their success, they suffer from some defects such as high storage requirements, high computational complexity, and low noise tolerance. In order to address these drawbacks, prototype selection has been studied as a technique to reduce the size of training datasets without deprecating the classification accuracy. Due to the need of achieving a trade-off between accuracy and reduction, Multi-Objective Evolutionary Algorithms (MOEAs) are emerging as methods efficient in solving the prototype selection problem. The goal of this paper is to perform a systematic comparison among well-known MOEAs in order to study their effects in solving this problem. The comparison involves the study of MOEAs' performance in terms of the well-known measures such as hypervolume, Δ index and coverage of two sets. The empirical analysis of the experimental results is validated through a statistical multiple comparison procedure.
{"title":"Comparison of Multi-objective Evolutionary Algorithms for prototype selection in nearest neighbor classification","authors":"G. Acampora, G. Tortora, A. Vitiello","doi":"10.1109/SSCI.2016.7849936","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849936","url":null,"abstract":"The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and good performance. However, in spite of their success, they suffer from some defects such as high storage requirements, high computational complexity, and low noise tolerance. In order to address these drawbacks, prototype selection has been studied as a technique to reduce the size of training datasets without deprecating the classification accuracy. Due to the need of achieving a trade-off between accuracy and reduction, Multi-Objective Evolutionary Algorithms (MOEAs) are emerging as methods efficient in solving the prototype selection problem. The goal of this paper is to perform a systematic comparison among well-known MOEAs in order to study their effects in solving this problem. The comparison involves the study of MOEAs' performance in terms of the well-known measures such as hypervolume, Δ index and coverage of two sets. The empirical analysis of the experimental results is validated through a statistical multiple comparison procedure.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134389929","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 : 2016-12-01DOI: 10.1109/SSCI.2016.7850266
Emer Bernal, O. Castillo, J. Soria
In this article we propose the use of fuzzy systems for dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation of existing solutions. In this paper we proposed distinct fuzzy systems for the dynamic adaptation of the c3 and c4 parameters to measure the performance of the algorithm with 17 benchmark functions with different number of dimensions. In this paper a comparison was made between different variants to prove the efficacy of the method in optimization problems.
{"title":"A fuzzy logic approach for dynamic adaptation of parameters in galactic swarm optimization","authors":"Emer Bernal, O. Castillo, J. Soria","doi":"10.1109/SSCI.2016.7850266","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850266","url":null,"abstract":"In this article we propose the use of fuzzy systems for dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation of existing solutions. In this paper we proposed distinct fuzzy systems for the dynamic adaptation of the c3 and c4 parameters to measure the performance of the algorithm with 17 benchmark functions with different number of dimensions. In this paper a comparison was made between different variants to prove the efficacy of the method in optimization problems.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134547347","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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