Pub Date : 2009-07-08DOI: 10.1109/CISDA.2009.5356533
R. D. Labati, V. Piuri, F. Scotti
The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems.
{"title":"Neural-based iterative approach for iris detection in iris recognition systems","authors":"R. D. Labati, V. Piuri, F. Scotti","doi":"10.1109/CISDA.2009.5356533","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356533","url":null,"abstract":"The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"328 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76547784","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356545
J. Connolly, Eric Granger, R. Sabourin
In many practical applications, new training data is acquired at different points in time, after a classification system has originally been trained. For instance, in face recognition systems, new training data may become available to enroll or to update knowledge of an individual. In this paper, a neural network classifier applied to video-based face recognition is adapted through supervised incremental learning of real-world video data. A training strategy based on particle swarm optimization is employed to co-optimize the weights, architecture and hyperparameters of the fuzzy ARTMAP network during incremental learning of new data. The performance of fuzzy ARTMAP is compared under different class update scenarios when incremental learning is performed according to 3 cases-(A) hyperparameters set to standard values, (B) hyperparameters optimized only at the beginning of the learning process with all classes, and (C) hyperparameters re-optimized whenever new training data becomes available. Overall results indicate that when samples from each individual enrolled to the system are employed for optimization, a higher classification rate is achieved and the solutions produced are more robust to variations caused by pattern presentation order. When all classes are refined equally, this is true with incremental learning according to case (C), whereas, if one class is refined at a time, best performance is obtained with case (B). However, optimizing hyperparameters requires more resources: several training sequences are needed to find the optimal solution and fuzzy ARTMAP with hyperparameters optimized according to classification rate tends to generate a high number of category nodes over longer convergence time.
{"title":"Incremental adaptation of fuzzy ARTMAP neural networks for video-based face classification","authors":"J. Connolly, Eric Granger, R. Sabourin","doi":"10.1109/CISDA.2009.5356545","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356545","url":null,"abstract":"In many practical applications, new training data is acquired at different points in time, after a classification system has originally been trained. For instance, in face recognition systems, new training data may become available to enroll or to update knowledge of an individual. In this paper, a neural network classifier applied to video-based face recognition is adapted through supervised incremental learning of real-world video data. A training strategy based on particle swarm optimization is employed to co-optimize the weights, architecture and hyperparameters of the fuzzy ARTMAP network during incremental learning of new data. The performance of fuzzy ARTMAP is compared under different class update scenarios when incremental learning is performed according to 3 cases-(A) hyperparameters set to standard values, (B) hyperparameters optimized only at the beginning of the learning process with all classes, and (C) hyperparameters re-optimized whenever new training data becomes available. Overall results indicate that when samples from each individual enrolled to the system are employed for optimization, a higher classification rate is achieved and the solutions produced are more robust to variations caused by pattern presentation order. When all classes are refined equally, this is true with incremental learning according to case (C), whereas, if one class is refined at a time, best performance is obtained with case (B). However, optimizing hyperparameters requires more resources: several training sequences are needed to find the optimal solution and fuzzy ARTMAP with hyperparameters optimized according to classification rate tends to generate a high number of category nodes over longer convergence time.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"55 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85645086","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356554
Amioy Kumar, M. Hanmandlu, H. Gupta
Online authentication is one of the basic requirements for any biometric based authentication system used in civil and commercial applications. This paper presents a new approach for online biometric authentication using hand vein patterns. In contrast to the existing approaches, our online authentication system utilizes infrared thermal images of hand vein patterns for authentication purposes. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns to convolve with Gabor filter for improving the visibility of vein pattern. The outcome of this convolution is the real and imaginary parts of which only the real part is regarded as a texture. Gabor Wavelets at different orientations are convolved with the real part after partitioning it into non-overlapping windows to extract texture. The mean of the convolution on each window is taken as a feature. The experimental results on 100 users conform to the false acceptance error rate (FAR) of 0.1% for the genuine acceptance rate (GAR) of 98.5%.
{"title":"Online biometric authentication using hand vein patterns","authors":"Amioy Kumar, M. Hanmandlu, H. Gupta","doi":"10.1109/CISDA.2009.5356554","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356554","url":null,"abstract":"Online authentication is one of the basic requirements for any biometric based authentication system used in civil and commercial applications. This paper presents a new approach for online biometric authentication using hand vein patterns. In contrast to the existing approaches, our online authentication system utilizes infrared thermal images of hand vein patterns for authentication purposes. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns to convolve with Gabor filter for improving the visibility of vein pattern. The outcome of this convolution is the real and imaginary parts of which only the real part is regarded as a texture. Gabor Wavelets at different orientations are convolved with the real part after partitioning it into non-overlapping windows to extract texture. The mean of the convolution on each window is taken as a feature. The experimental results on 100 users conform to the false acceptance error rate (FAR) of 0.1% for the genuine acceptance rate (GAR) of 98.5%.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"39 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87463503","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356550
R. Kannavara, N. Bourbakis
In this work, we present a Local-Global (LG) graph methodology for iris based biometric authentication. Local-Global (LG) graph method adds local part information into a global graph. Local graphs of the pre-processed iris images are first calculated by feature extraction and combined to form a Global graph that is stored in a database for the purpose of authentication. The Global graph of the presented test image is compared with the Global graph of the stored reference image and based on a distance metric, the authenticity of the subject is established.
{"title":"Iris biometric authentication based on local global graphs: An FPGA implementation","authors":"R. Kannavara, N. Bourbakis","doi":"10.1109/CISDA.2009.5356550","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356550","url":null,"abstract":"In this work, we present a Local-Global (LG) graph methodology for iris based biometric authentication. Local-Global (LG) graph method adds local part information into a global graph. Local graphs of the pre-processed iris images are first calculated by feature extraction and combined to form a Global graph that is stored in a database for the purpose of authentication. The Global graph of the presented test image is compared with the Global graph of the stored reference image and based on a distance metric, the authenticity of the subject is established.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"14 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82106917","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356531
D. Gorodnichy
Biometric systems have evolved significantly over the past years: from single-sample fully-controlled verification matchers to a wide range of multi-sample multi-modal fully-automated person recognition systems working in a diverse range of unconstrained environments and behaviors. The methodology for biometric system evaluation however has remained practically unchanged, still being largely limited to reporting false match and non-match rates only and the tradeoff curves based thereon. Such methodology may no longer be sufficient and appropriate for investigating the performance of state-of-the-art systems. This paper addresses this gap by establishing taxonomy of biometric systems and proposing a baseline methodology that can be applied to the majority of contemporary biometric systems to obtain an all-inclusive description of their performance. In doing that, a novel concept of multi-order performance analysis is introduced and the results obtained from a large-scale iris biometric system examination are presented.
{"title":"Evolution and evaluation of biometric systems","authors":"D. Gorodnichy","doi":"10.1109/CISDA.2009.5356531","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356531","url":null,"abstract":"Biometric systems have evolved significantly over the past years: from single-sample fully-controlled verification matchers to a wide range of multi-sample multi-modal fully-automated person recognition systems working in a diverse range of unconstrained environments and behaviors. The methodology for biometric system evaluation however has remained practically unchanged, still being largely limited to reporting false match and non-match rates only and the tradeoff curves based thereon. Such methodology may no longer be sufficient and appropriate for investigating the performance of state-of-the-art systems. This paper addresses this gap by establishing taxonomy of biometric systems and proposing a baseline methodology that can be applied to the majority of contemporary biometric systems to obtain an all-inclusive description of their performance. In doing that, a novel concept of multi-order performance analysis is introduced and the results obtained from a large-scale iris biometric system examination are presented.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"8 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82576209","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356541
P. LaRoche, A. N. Zincir-Heywood, M. Heywood
In this work, we investigate the ability of genetic programming techniques to evolve valid network packets, including all relevant header values, towards a specific goal. We see this as a first step in building a fuzzing system that can learn to adapt for vulnerability analysis. By developing a system that learns the packets that are required to be transmitted towards targets, using feedback from an external network source, we make a step towards having a system that can intelligently explore the capabilities of a given security system. In order to validate our system's capabilities we evolve a variety of port scan patterns while running the packets through an IDS, with the goal to minimizes the alarms raised during the scanning process. Results show that the system not only successfully evolves valid TCP packets, but also remains stealthy in its activity.
{"title":"Evolving TCP/IP packets: A case study of port scans","authors":"P. LaRoche, A. N. Zincir-Heywood, M. Heywood","doi":"10.1109/CISDA.2009.5356541","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356541","url":null,"abstract":"In this work, we investigate the ability of genetic programming techniques to evolve valid network packets, including all relevant header values, towards a specific goal. We see this as a first step in building a fuzzing system that can learn to adapt for vulnerability analysis. By developing a system that learns the packets that are required to be transmitted towards targets, using feedback from an external network source, we make a step towards having a system that can intelligently explore the capabilities of a given security system. In order to validate our system's capabilities we evolve a variety of port scan patterns while running the packets through an IDS, with the goal to minimizes the alarms raised during the scanning process. Results show that the system not only successfully evolves valid TCP packets, but also remains stealthy in its activity.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"2 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89530061","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356540
F. A. Alsulaiman, N. Sakr, J. J. Valdés, Abdulmotaleb El Saddik, N. Georganas
In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.
{"title":"Feature selection and classification in genetic programming: Application to haptic-based biometric data","authors":"F. A. Alsulaiman, N. Sakr, J. J. Valdés, Abdulmotaleb El Saddik, N. Georganas","doi":"10.1109/CISDA.2009.5356540","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356540","url":null,"abstract":"In this paper, a study is conducted in order to explore the use of genetic programming, in particular gene expression programming (GEP), in finding analytic functions that can behave as classifiers in high-dimensional haptic feature spaces. More importantly, the determined explicit functions are used in discovering minimal knowledge-preserving subsets of features from very high dimensional haptic datasets, thus acting as general dimensionality reducers. This approach is applied to the haptic-based biometrics problem; namely, in user identity verification. GEP models are initially generated using the original haptic biometric datatset, which is imbalanced in terms of the number of representative instances of each class. This procedure was repeated while considering an under-sampled (balanced) version of the datasets. The results demonstrated that for all datasets, whether imbalanced or under-sampled, a certain number (on average) of perfect classification models were determined. In addition, using GEP, great feature reduction was achieved as the generated analytic functions (classifiers) exploited only a small fraction of the available features.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"34 12 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83007294","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356527
I. Pestov, S. Verga
This paper attempts to bring together methods and tools across three selected areas of Computational Intelligence pertaining to complex systems: dynamical networks, statistical machine learning, and multi-agent technologies. The paper begins with a discussion of computational challenges arising from growing complexity in modern operating environments. Then the discussion proceeds to an overview of intelligent tools that can be used to address these challenges. The emphasis is given to dynamical networks as the means for consolidating ideas and approaches. An illustrative example shows how the network-based tools can be used to model complex socio-technical systems, to identify hidden interdependencies among their components, and to explore their vulnerabilities in simulations.
{"title":"Dynamical networks as a tool for system analysis and exploration","authors":"I. Pestov, S. Verga","doi":"10.1109/CISDA.2009.5356527","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356527","url":null,"abstract":"This paper attempts to bring together methods and tools across three selected areas of Computational Intelligence pertaining to complex systems: dynamical networks, statistical machine learning, and multi-agent technologies. The paper begins with a discussion of computational challenges arising from growing complexity in modern operating environments. Then the discussion proceeds to an overview of intelligent tools that can be used to address these challenges. The emphasis is given to dynamical networks as the means for consolidating ideas and approaches. An illustrative example shows how the network-based tools can be used to model complex socio-technical systems, to identify hidden interdependencies among their components, and to explore their vulnerabilities in simulations.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"71 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84934374","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356553
R. Elias, A. Elnahas
In this paper, we propose an algorithm for fast indoor localization. The algorithm does not require any sensors to be installed; instead, localization is determined using image matching. Our system studies (or learns) the indoor environment through detecting image junctions using the so-called JUDOCA detector. Any 2-edge junction forms a triangle that can be used to store information and recognize the environment afterwards. Correlation is applied to points denoted with respect to one side of the triangle formed by the junction. Experiments show that this approach reaches similar accuracy of the affine-based correlation approach in less processing time.
{"title":"Fast localization in indoor environments","authors":"R. Elias, A. Elnahas","doi":"10.1109/CISDA.2009.5356553","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356553","url":null,"abstract":"In this paper, we propose an algorithm for fast indoor localization. The algorithm does not require any sensors to be installed; instead, localization is determined using image matching. Our system studies (or learns) the indoor environment through detecting image junctions using the so-called JUDOCA detector. Any 2-edge junction forms a triangle that can be used to store information and recognize the environment afterwards. Correlation is applied to points denoted with respect to one side of the triangle formed by the junction. Experiments show that this approach reaches similar accuracy of the affine-based correlation approach in less processing time.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"45 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85247127","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 : 2009-07-08DOI: 10.1109/CISDA.2009.5356525
M. Mazurek, S. Wesolkowski
We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to perform a multiobjective optimization of the Stochastic Fleet Estimation (SaFE) model. SaFE is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. We search for Pareto-optimal combinations of valid platform-assignments for a list of tasks, which can be applied to complete scenarios output by SaFE. Solutions are evaluated on three objectives, with the goal of minimizing fleet cost, total task duration time, and the risk that a solution will not be able to accomplish possible future scenarios.
我们应用非支配排序遗传算法- ii (NSGA-II)对随机舰队估计(SaFE)模型进行多目标优化。SaFE是一个基于蒙特卡罗的模型，它根据车队应该完成的要求集生成车队。我们为一组任务搜索有效平台分配的帕累托最优组合，这些任务可以应用于由SaFE输出的完整场景。解决方案根据三个目标进行评估，目标是最小化车队成本、总任务持续时间和解决方案无法完成未来可能场景的风险。
{"title":"Minimizing risk on a fleet mix problem with a multiobjective evolutionary algorithm","authors":"M. Mazurek, S. Wesolkowski","doi":"10.1109/CISDA.2009.5356525","DOIUrl":"https://doi.org/10.1109/CISDA.2009.5356525","url":null,"abstract":"We apply the non-dominated sorting genetic algorithm-II (NSGA-II) to perform a multiobjective optimization of the Stochastic Fleet Estimation (SaFE) model. SaFE is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. We search for Pareto-optimal combinations of valid platform-assignments for a list of tasks, which can be applied to complete scenarios output by SaFE. Solutions are evaluated on three objectives, with the goal of minimizing fleet cost, total task duration time, and the risk that a solution will not be able to accomplish possible future scenarios.","PeriodicalId":6407,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications","volume":"454 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2009-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82941461","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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