Pub Date : 2014-10-20DOI: 10.1109/UKCI.2014.6930176
I. Sakellariou, O. Kurdi, M. Gheorghe, Daniela Romano, P. Kefalas, F. Ipate, Ionut-Mihai Niculescu
There is an increasing interest in modelling of agents interacting as crowd and a simulation of such scenarios that map to real-life situations. This paper presents a generic state-based abstract model for crowd behaviour that can be mapped onto different agent-based systems. In particular, the abstract model is mapped into the simulation framework NetLogo. We have used the model to simulate a real-life case study of high density diverse crowd such as the Hajj ritual at the mosque in Mecca (Makkah). The computational model is based on real data extracted from videos of the ritual. We also present a methodology for extracting significant data, parameters, and patterns of behaviour from real-world videos that has been used as an early stage validation to demonstrate that the obtained simulations are realistic.
{"title":"Crowd formal modelling and simulation: The Sa'yee ritual","authors":"I. Sakellariou, O. Kurdi, M. Gheorghe, Daniela Romano, P. Kefalas, F. Ipate, Ionut-Mihai Niculescu","doi":"10.1109/UKCI.2014.6930176","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930176","url":null,"abstract":"There is an increasing interest in modelling of agents interacting as crowd and a simulation of such scenarios that map to real-life situations. This paper presents a generic state-based abstract model for crowd behaviour that can be mapped onto different agent-based systems. In particular, the abstract model is mapped into the simulation framework NetLogo. We have used the model to simulate a real-life case study of high density diverse crowd such as the Hajj ritual at the mosque in Mecca (Makkah). The computational model is based on real data extracted from videos of the ritual. We also present a methodology for extracting significant data, parameters, and patterns of behaviour from real-world videos that has been used as an early stage validation to demonstrate that the obtained simulations are realistic.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128109716","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930170
Tianhua Chen, Q. Shen, P. Su, C. Shang
The most challenging problem in the design of fuzzy rule-based classification systems is the construction of a fuzzy rule base for the target problem. Much research has focused on generating and adjusting antecedent fuzzy sets. In many cases, initial fuzzy sets, each of which has a linguistic meaning, are predefined by domain experts and are thus required to be maintained in order to ensure interpretability of any subsequent inference results. However, learning fuzzy rules using fixed fuzzy quantity space without any quantification will restrict the accuracy of the resulting rules. Fortunately, adjusting the weight of a fuzzy if-then rule may help improve classification accuracy without degrading the interpretability. There have been different proposals for fuzzy rule weight tuning through the use of various heuristics with limited success. This paper proposes an alternative approach using Particle Swarm Optimisation in the search of a set of optimal rule weights, which can entail high classification accuracy. The proposed method is initially tested on the iris data set with regard to different predefined fuzzy partitions of linguistic variables to assess its performance. Experimental results demonstrate that the proposed approach is not sensitive to the predefined fuzzy partitions, and can boost classification performance especially when a coarse fuzzy partition is given.
{"title":"Refinement of fuzzy rule weights with particle swarm optimisation","authors":"Tianhua Chen, Q. Shen, P. Su, C. Shang","doi":"10.1109/UKCI.2014.6930170","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930170","url":null,"abstract":"The most challenging problem in the design of fuzzy rule-based classification systems is the construction of a fuzzy rule base for the target problem. Much research has focused on generating and adjusting antecedent fuzzy sets. In many cases, initial fuzzy sets, each of which has a linguistic meaning, are predefined by domain experts and are thus required to be maintained in order to ensure interpretability of any subsequent inference results. However, learning fuzzy rules using fixed fuzzy quantity space without any quantification will restrict the accuracy of the resulting rules. Fortunately, adjusting the weight of a fuzzy if-then rule may help improve classification accuracy without degrading the interpretability. There have been different proposals for fuzzy rule weight tuning through the use of various heuristics with limited success. This paper proposes an alternative approach using Particle Swarm Optimisation in the search of a set of optimal rule weights, which can entail high classification accuracy. The proposed method is initially tested on the iris data set with regard to different predefined fuzzy partitions of linguistic variables to assess its performance. Experimental results demonstrate that the proposed approach is not sensitive to the predefined fuzzy partitions, and can boost classification performance especially when a coarse fuzzy partition is given.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127367745","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930191
M. Al-Taee, Suhail N. Abood, W. Al-Nuaimy, Ahmad M. Al-Taee
Pattern recognition has been an effective approach to identifying glycaemic patterns within self-monitored blood glucose (BG) data in diabetes mellitus patients. This paper presents a new BG pattern mining algorithm for more targeted therapeutic decision support in diabetes self-management. Based on patients' BG readings which are collected via a handheld device and logged on a web-based health portal, the existing BG patterns are extracted in real-time and fed back to the patient along with appropriate therapeutic recommendations, educational modules and health care advice. The identified patterns help patients improve their blood glucose management and education about diabetes and its complications. A functional prototype of the proposed system is developed and its end-to-end functionality is successfully demonstrated. A pilot clinical study demonstrated positive user acceptability and interest in its decision support attributes for diabetes self-management, making this a promising avenue for further research.
{"title":"Blood-glucose pattern mining algorithm for decision support in diabetes management","authors":"M. Al-Taee, Suhail N. Abood, W. Al-Nuaimy, Ahmad M. Al-Taee","doi":"10.1109/UKCI.2014.6930191","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930191","url":null,"abstract":"Pattern recognition has been an effective approach to identifying glycaemic patterns within self-monitored blood glucose (BG) data in diabetes mellitus patients. This paper presents a new BG pattern mining algorithm for more targeted therapeutic decision support in diabetes self-management. Based on patients' BG readings which are collected via a handheld device and logged on a web-based health portal, the existing BG patterns are extracted in real-time and fed back to the patient along with appropriate therapeutic recommendations, educational modules and health care advice. The identified patterns help patients improve their blood glucose management and education about diabetes and its complications. A functional prototype of the proposed system is developed and its end-to-end functionality is successfully demonstrated. A pilot clinical study demonstrated positive user acceptability and interest in its decision support attributes for diabetes self-management, making this a promising avenue for further research.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122354373","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930169
D. Olczyk, Urszula Markowska-Kaczmar
The Fuzzy Set Parameter Estimation algorithm is proposed for fast learning interval type-2 fuzzy neural networks applied for classification problems. Classes are disjoint. Learning consists of estimating appropriate values of fuzzy set parameters in every rule. Estimation is based on statistical properties of the training data. The experimental study confirms that it is dozens times quicker than the backpropagation method, while the classification effectiveness is comparable.
{"title":"Fast learning method of interval type-2 fuzzy neural networks","authors":"D. Olczyk, Urszula Markowska-Kaczmar","doi":"10.1109/UKCI.2014.6930169","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930169","url":null,"abstract":"The Fuzzy Set Parameter Estimation algorithm is proposed for fast learning interval type-2 fuzzy neural networks applied for classification problems. Classes are disjoint. Learning consists of estimating appropriate values of fuzzy set parameters in every rule. Estimation is based on statistical properties of the training data. The experimental study confirms that it is dozens times quicker than the backpropagation method, while the classification effectiveness is comparable.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643284","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930194
P. Mistry, Anna Palczewska, D. Neagu, P. Trundle
Drug vehicles are chemical carriers that aid a drug's passage through an organism. Whilst they possess no intrinsic efficacy they are designed to achieve desirable characteristics which can include improving a drug's permeability and or solubility, targeting a drug to a specific site or reducing a drug's toxicity. All of which are ideally achieved without compromising the efficacy of the drug. Whilst the majority of drug vehicle research is focused on the solubility and permeability issues of a drug, significant progress has been made on using vehicles for toxicity reduction. Achieving this can enable safer and more effective use of a potent drug against diseases such as cancer. From a molecular perspective, drugs activate or deactivate biochemical pathways through interactions with cellular macromolecules resulting in toxicity. For newly developed drugs such pathways are not always clearly understood but toxicity endpoints are still required as part of a drug's registration. An understanding of which vehicles could be used to ameliorate the unwanted toxicities of newly developed drugs would be highly desirable to the pharmaceutical industry. In this paper we demonstrate the use of different classifiers as a means to select vehicles best suited to avert a drug's toxic effects when no other information about a drug's characteristics is known. Through analysis of data acquired from the Developmental Therapeutics Program (DTP) we are able to establish a link between a drug's toxicity and vehicle used. We demonstrate that classification and selection of the appropriate vehicle can be made based on the similarity of drug choice.
{"title":"Using computational methods for the prediction of drug vehicles","authors":"P. Mistry, Anna Palczewska, D. Neagu, P. Trundle","doi":"10.1109/UKCI.2014.6930194","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930194","url":null,"abstract":"Drug vehicles are chemical carriers that aid a drug's passage through an organism. Whilst they possess no intrinsic efficacy they are designed to achieve desirable characteristics which can include improving a drug's permeability and or solubility, targeting a drug to a specific site or reducing a drug's toxicity. All of which are ideally achieved without compromising the efficacy of the drug. Whilst the majority of drug vehicle research is focused on the solubility and permeability issues of a drug, significant progress has been made on using vehicles for toxicity reduction. Achieving this can enable safer and more effective use of a potent drug against diseases such as cancer. From a molecular perspective, drugs activate or deactivate biochemical pathways through interactions with cellular macromolecules resulting in toxicity. For newly developed drugs such pathways are not always clearly understood but toxicity endpoints are still required as part of a drug's registration. An understanding of which vehicles could be used to ameliorate the unwanted toxicities of newly developed drugs would be highly desirable to the pharmaceutical industry. In this paper we demonstrate the use of different classifiers as a means to select vehicles best suited to avert a drug's toxic effects when no other information about a drug's characteristics is known. Through analysis of data acquired from the Developmental Therapeutics Program (DTP) we are able to establish a link between a drug's toxicity and vehicle used. We demonstrate that classification and selection of the appropriate vehicle can be made based on the similarity of drug choice.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249024","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930167
Warren G. Jackson, E. Özcan, R. John
A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies on two core components, a method for selecting a heuristic to apply at a given point, and a method to decide whether or not to accept the result of the heuristic application. In this paper, we present an initial study of a fuzzy system to control the list-size parameter of late-acceptance move acceptance method as a selection hyper-heuristic component. The performance of the fuzzy controlled selection hyper-heuristic is compared to its fixed parameter version and the best hyper-heuristic from a competition on the MAX-SAT problem domain. The results illustrate that a fuzzy control system can potentially be effective within a hyper-heuristic improving its performance.
{"title":"Fuzzy adaptive parameter control of a late acceptance hyper-heuristic","authors":"Warren G. Jackson, E. Özcan, R. John","doi":"10.1109/UKCI.2014.6930167","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930167","url":null,"abstract":"A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies on two core components, a method for selecting a heuristic to apply at a given point, and a method to decide whether or not to accept the result of the heuristic application. In this paper, we present an initial study of a fuzzy system to control the list-size parameter of late-acceptance move acceptance method as a selection hyper-heuristic component. The performance of the fuzzy controlled selection hyper-heuristic is compared to its fixed parameter version and the best hyper-heuristic from a competition on the MAX-SAT problem domain. The results illustrate that a fuzzy control system can potentially be effective within a hyper-heuristic improving its performance.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141227","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930154
G. Rigatos, G. Raffo
The paper proposes a nonlinear control approach for the underactuated hovercraft model based on differential flatness theory and using a new nonlinear state vector and disturbances estimation method under the name of Derivative-free nonlinear Kalman Filter. It is proven that the sixth order nonlinear model of the hovercraft is a differentially flat one. It is shown that this model cannot be subjected to static feedback linearization, however it admits dynamic feedback linearization which means that the system's state vector is extended by including as additional state variables the control inputs and their derivatives. Next, using the differential flatness properties it is also proven that this model can be subjected to input-output linearization and can be transformed to an equivalent canonical (Brunovsky) form. Based on this latter description the design of a state feedback controller is carried out enabling accurate maneuvering and trajectory tracking. Additional problems that are solved in the design of this feedback control scheme are the estimation of the nonmeasurable state variables in the hovercraft's model and the compensation of modeling uncertainties and external perturbations affecting vessel. To this end, the application of the Derivative-free nonlinear Kalman Filter is proposed. This nonlinear filter consists of the Kalman Filter's recursion on the linearized equivalent of the vessel and of an inverse nonlinear transformation based on the differential flatness features of the system which enables to compute state estimates for the state variables of the initial nonlinear model. The redesign of the filter as a disturbance observer makes possible the estimation and compensation of additive perturbation terms affecting the hovercraft's model. The efficiency of the proposed nonlinear control and state estimation scheme is confirmed through simulation experiments.
{"title":"Nonlinear control of the underactuated hovercraft using the Derivative-free nonlinear Kalman filter","authors":"G. Rigatos, G. Raffo","doi":"10.1109/UKCI.2014.6930154","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930154","url":null,"abstract":"The paper proposes a nonlinear control approach for the underactuated hovercraft model based on differential flatness theory and using a new nonlinear state vector and disturbances estimation method under the name of Derivative-free nonlinear Kalman Filter. It is proven that the sixth order nonlinear model of the hovercraft is a differentially flat one. It is shown that this model cannot be subjected to static feedback linearization, however it admits dynamic feedback linearization which means that the system's state vector is extended by including as additional state variables the control inputs and their derivatives. Next, using the differential flatness properties it is also proven that this model can be subjected to input-output linearization and can be transformed to an equivalent canonical (Brunovsky) form. Based on this latter description the design of a state feedback controller is carried out enabling accurate maneuvering and trajectory tracking. Additional problems that are solved in the design of this feedback control scheme are the estimation of the nonmeasurable state variables in the hovercraft's model and the compensation of modeling uncertainties and external perturbations affecting vessel. To this end, the application of the Derivative-free nonlinear Kalman Filter is proposed. This nonlinear filter consists of the Kalman Filter's recursion on the linearized equivalent of the vessel and of an inverse nonlinear transformation based on the differential flatness features of the system which enables to compute state estimates for the state variables of the initial nonlinear model. The redesign of the filter as a disturbance observer makes possible the estimation and compensation of additive perturbation terms affecting the hovercraft's model. The efficiency of the proposed nonlinear control and state estimation scheme is confirmed through simulation experiments.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130583640","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930177
Türker Erçal, E. Özcan, Shahriar Asta
Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well.
{"title":"Soft morphological filter optimization using a genetic algorithm for noise elimination","authors":"Türker Erçal, E. Özcan, Shahriar Asta","doi":"10.1109/UKCI.2014.6930177","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930177","url":null,"abstract":"Digital image quality is of importance in almost all image processing applications. Many different approaches have been proposed for restoring the image quality depending on the nature of the degradation. One of the most common problems that cause such degradation is impulse noise. In general, well known median filters are preferred for eliminating different types of noise. Soft morphological filters are recently introduced and have been in use for many purposes. In this study, we present a Genetic Algorithm (GA) which combines different objectives as a weighted sum under a single evaluation function and generates a soft morphological filter to deal with impulse noise, after a training process with small images. The automatically generated filter performs better than the median filter and achieves comparable results to the best known filters from the literature over a set of benchmark instances that are larger than the training instances. Moreover, although the training process involves only impulse noise added images, the same evolved filter performs better than the median filter for eliminating Gaussian noise as well.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122632928","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930168
Seda Türk, R. John, E. Özcan
Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. This study introduces a model for solving the supplier selection problem using interval type-2 fuzzy sets. Moreover, the influence of the membership function shape on the results obtained from the model has been investigated on a real-world problem instance tackled by Ordoobadi.
{"title":"Interval type-2 fuzzy sets in supplier selection","authors":"Seda Türk, R. John, E. Özcan","doi":"10.1109/UKCI.2014.6930168","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930168","url":null,"abstract":"Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. This study introduces a model for solving the supplier selection problem using interval type-2 fuzzy sets. Moreover, the influence of the membership function shape on the results obtained from the model has been investigated on a real-world problem instance tackled by Ordoobadi.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120999722","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 : 2014-10-20DOI: 10.1109/UKCI.2014.6930175
Chigozirim J. Uzor, M. Gongora, S. Coupland, Benjamin N. Passow
The interest in nature inspired optimization in dynamic environments has been increasing constantly over the past years. This trend implies that many real world problems experience dynamic changes and it is important to develop an efficient algorithm capable of tackling these problems. Several techniques have been developed over the past two decades for solving dynamic optimization problems. Among these techniques, the hypermutation scheme has proved to be beneficial in solving some of the dynamic optimization problems but requires that the mutation factors be picked a priori. This paper investigates a new mutation and change detection scheme for compact genetic algorithm (cGA), where the degree of change regulates the mutation rate (i.e. mutation rate is directly proportional to the degree of change). The experimental results shows that the mutation and change detection scheme has an impact on the performance of the cGA in dynamic environments and that the effect of the proposed scheme depends on the dynamics of the environment.
{"title":"Adaptive mutation in dynamic environments","authors":"Chigozirim J. Uzor, M. Gongora, S. Coupland, Benjamin N. Passow","doi":"10.1109/UKCI.2014.6930175","DOIUrl":"https://doi.org/10.1109/UKCI.2014.6930175","url":null,"abstract":"The interest in nature inspired optimization in dynamic environments has been increasing constantly over the past years. This trend implies that many real world problems experience dynamic changes and it is important to develop an efficient algorithm capable of tackling these problems. Several techniques have been developed over the past two decades for solving dynamic optimization problems. Among these techniques, the hypermutation scheme has proved to be beneficial in solving some of the dynamic optimization problems but requires that the mutation factors be picked a priori. This paper investigates a new mutation and change detection scheme for compact genetic algorithm (cGA), where the degree of change regulates the mutation rate (i.e. mutation rate is directly proportional to the degree of change). The experimental results shows that the mutation and change detection scheme has an impact on the performance of the cGA in dynamic environments and that the effect of the proposed scheme depends on the dynamics of the environment.","PeriodicalId":315044,"journal":{"name":"2014 14th UK Workshop on Computational Intelligence (UKCI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116062831","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：