Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586128
James Montgomery, Stephen Y. Chen
A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr ∊ [0, 1]. While very low values are recommended for and used with separable problems, on non-separable problems, which include most real-world problems, Cr = 0.9 has become the de facto standard, working well across a large range of problem domains. Recent work on separable and non-separable problems has shown that lower-dimensional searches can play an important role in the performance of search techniques in higher-dimensional search spaces. However, the standard value of Cr = 0.9 implies a very high-dimensional search, which is not effective for other search techniques. An analysis of Cr across its range [0, 1] provides insight into how its value affects the performance of DE and suggests how low values may be used to improve the performance of DE. This new understanding of the operation of DE at high and low crossover rates is useful for analysing how adaptive parameters affect DE performance and leads to new suggestions for how adaptive DE techniques might be developed.
{"title":"An analysis of the operation of differential evolution at high and low crossover rates","authors":"James Montgomery, Stephen Y. Chen","doi":"10.1109/CEC.2010.5586128","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586128","url":null,"abstract":"A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr ∊ [0, 1]. While very low values are recommended for and used with separable problems, on non-separable problems, which include most real-world problems, Cr = 0.9 has become the de facto standard, working well across a large range of problem domains. Recent work on separable and non-separable problems has shown that lower-dimensional searches can play an important role in the performance of search techniques in higher-dimensional search spaces. However, the standard value of Cr = 0.9 implies a very high-dimensional search, which is not effective for other search techniques. An analysis of Cr across its range [0, 1] provides insight into how its value affects the performance of DE and suggests how low values may be used to improve the performance of DE. This new understanding of the operation of DE at high and low crossover rates is useful for analysing how adaptive parameters affect DE performance and leads to new suggestions for how adaptive DE techniques might be developed.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75678241","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 : 2010-07-18DOI: 10.1109/CEC.2010.5585962
Ali Vahdat, M. Heywood, A. N. Zincir-Heywood
The ultimate goal of subspace clustering algorithms is to identify both the subset of attributes supporting a cluster and the location of the cluster in the subspace. In this work a generic evolutionary approach to bottom-up subspace clustering is proposed consisting of three steps. The first applies a non-evolutionary clustering algorithm attribute-wise to establish the lattice from which subspace clusters will be designed. In the second step a multi-objective Genetic Algorithm (MOGA) is used to evolve good candidate subspace clusters (CSC) through a combinatorial search w.r.t. the attribute-wise lattice from step 1. The third step then searches in the space of CSC from the population of the the first MOGA to find the best combination of subspace clusters, again under a MOGA formulation. Important properties of the approach are that a standard clustering algorithm is deployed in step one to build the initial lattice of attribute-wise clusters. This helps to decouple the computational expense of clustering using Evolutionary Computation, with the MOGA applied in steps 2 and 3 building clusters through a combinatorial search relative to the original lattice parameters. Benchmarking on data sets with tens to hundreds of attributes illustrates the feasibility of the approach.
{"title":"Bottom-up evolutionary subspace clustering","authors":"Ali Vahdat, M. Heywood, A. N. Zincir-Heywood","doi":"10.1109/CEC.2010.5585962","DOIUrl":"https://doi.org/10.1109/CEC.2010.5585962","url":null,"abstract":"The ultimate goal of subspace clustering algorithms is to identify both the subset of attributes supporting a cluster and the location of the cluster in the subspace. In this work a generic evolutionary approach to bottom-up subspace clustering is proposed consisting of three steps. The first applies a non-evolutionary clustering algorithm attribute-wise to establish the lattice from which subspace clusters will be designed. In the second step a multi-objective Genetic Algorithm (MOGA) is used to evolve good candidate subspace clusters (CSC) through a combinatorial search w.r.t. the attribute-wise lattice from step 1. The third step then searches in the space of CSC from the population of the the first MOGA to find the best combination of subspace clusters, again under a MOGA formulation. Important properties of the approach are that a standard clustering algorithm is deployed in step one to build the initial lattice of attribute-wise clusters. This helps to decouple the computational expense of clustering using Evolutionary Computation, with the MOGA applied in steps 2 and 3 building clusters through a combinatorial search relative to the original lattice parameters. Benchmarking on data sets with tens to hundreds of attributes illustrates the feasibility of the approach.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75702266","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586075
M. Pilát, Roman Neruda
The majority of multiobjective genetic algorithms is computationally expensive, therefore they often need to be parallelized before they can be used to solve practical tasks. Parallelization of multiobjective genetic algorithms is a relatively studied area, but no clearly winning approach has appeared yet. In this paper we present a novel parallel hybrid algorithm which combines multiobjective and single-objective genetic algorithms. We show that this algorithm can be successfully used to solve multiobjective optimization problems while outperforming more traditional parallel versions of multiobjective genetic algorithms.
{"title":"Combining multiobjective and single-objective genetic algorithms in heterogeneous island model","authors":"M. Pilát, Roman Neruda","doi":"10.1109/CEC.2010.5586075","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586075","url":null,"abstract":"The majority of multiobjective genetic algorithms is computationally expensive, therefore they often need to be parallelized before they can be used to solve practical tasks. Parallelization of multiobjective genetic algorithms is a relatively studied area, but no clearly winning approach has appeared yet. In this paper we present a novel parallel hybrid algorithm which combines multiobjective and single-objective genetic algorithms. We show that this algorithm can be successfully used to solve multiobjective optimization problems while outperforming more traditional parallel versions of multiobjective genetic algorithms.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73705806","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}
Organisms have better adaptability that computer systems in dealing with environmental changes or noise. A close structure-function relation inherent in biological structures is an important feature for providing great malleability to environmental changes. By contrast, computers have fast processing speeds but with limited adaptability. A biologically motivated model (hardware design) that combines intra-and inter-neuronal information processing implemented with digital circuit was proposed. Pattern recognition was the present application domain. The circuit was tested with Quartus II system, a digital circuit simulation tool. The experimental result showed that the artificial neuromolecularware (ANM) exhibited a close structure-function relationship, possessed several evolvability-enhancing features combined to facilitate evolutionary learning, and was capable of functioning continuously in the face of noise.
{"title":"Enhancing digital hardware evolvability with a neuromolecularware design: A biologically-motivated approach","authors":"Yo-Hsien Lin, Jong-Chen Chen, Wei-Chang Lee, Chung-Chian Hsu","doi":"10.1109/CEC.2010.5586228","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586228","url":null,"abstract":"Organisms have better adaptability that computer systems in dealing with environmental changes or noise. A close structure-function relation inherent in biological structures is an important feature for providing great malleability to environmental changes. By contrast, computers have fast processing speeds but with limited adaptability. A biologically motivated model (hardware design) that combines intra-and inter-neuronal information processing implemented with digital circuit was proposed. Pattern recognition was the present application domain. The circuit was tested with Quartus II system, a digital circuit simulation tool. The experimental result showed that the artificial neuromolecularware (ANM) exhibited a close structure-function relationship, possessed several evolvability-enhancing features combined to facilitate evolutionary learning, and was capable of functioning continuously in the face of noise.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78709648","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586393
Y. Nojima, S. Mihara, H. Ishibuchi
Evolutionary algorithms have been actively applied to knowledge discovery, data mining and machine learning under the name of genetics-based machine learning (GBML). The main advantage of using evolutionary algorithms in those application areas is their flexibility: Various knowledge extraction criteria such as accuracy and complexity can be easily utilized as fitness functions. On the other hand, the main disadvantage is their large computation load. It is not easy to apply evolutionary algorithms to large data sets. The scalability improvement to large data sets is one of the main research issues in GBML. In our former studies, we proposed an idea of parallel distributed implementation of GBML and examined its effectiveness for genetic fuzzy rule selection. The point of our idea was to realize a quadratic speed-up by dividing not only a population but also training data. Training data subsets were periodically rotated over sub-populations in order to prevent each sub-population from over-fitting to a specific training data subset. In this paper, we propose the use of parallel distributed implementation for the design of ensemble classifiers. An ensemble classifier is designed by combining base classifiers, each of which is obtained from each sub-population. Through computational experiments on parallel distributed genetic fuzzy rule selection, we examine the generalization ability of designed ensemble classifiers under various settings with respect to the size of training data subsets and their rotation frequency.
{"title":"Ensemble classifier design by parallel distributed implementation of genetic fuzzy rule selection for large data sets","authors":"Y. Nojima, S. Mihara, H. Ishibuchi","doi":"10.1109/CEC.2010.5586393","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586393","url":null,"abstract":"Evolutionary algorithms have been actively applied to knowledge discovery, data mining and machine learning under the name of genetics-based machine learning (GBML). The main advantage of using evolutionary algorithms in those application areas is their flexibility: Various knowledge extraction criteria such as accuracy and complexity can be easily utilized as fitness functions. On the other hand, the main disadvantage is their large computation load. It is not easy to apply evolutionary algorithms to large data sets. The scalability improvement to large data sets is one of the main research issues in GBML. In our former studies, we proposed an idea of parallel distributed implementation of GBML and examined its effectiveness for genetic fuzzy rule selection. The point of our idea was to realize a quadratic speed-up by dividing not only a population but also training data. Training data subsets were periodically rotated over sub-populations in order to prevent each sub-population from over-fitting to a specific training data subset. In this paper, we propose the use of parallel distributed implementation for the design of ensemble classifiers. An ensemble classifier is designed by combining base classifiers, each of which is obtained from each sub-population. Through computational experiments on parallel distributed genetic fuzzy rule selection, we examine the generalization ability of designed ensemble classifiers under various settings with respect to the size of training data subsets and their rotation frequency.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78753056","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586235
J. Kruisselbrink, M. Emmerich, A. Deutz, Thomas Bäck
This paper presents a study for using Kriging metamodeling in combination with Covariance Matrix Adaptation Evolution Strategies (CMA-ES) to find robust solutions. A general, archive based, framework is proposed for integrating Kriging within CMA-ES, including a method to utilize the covariance matrix of the CMA-ES in a straightforward way to improve the accuracy of the Kriging predictions without introducing much additional computational cost. Moreover, it adopts an elegant way to select appropriate archive points for building a local metamodel. The study shows that this Kriging metamodeling scheme for finding robust solutions outperforms common, straightforward approaches and is very useful when there is a limited budget of function evaluations. Though using the covariance matrix can improve the prediction quality, it has no significant effect on the overall quality of the optimization results.
{"title":"A robust optimization approach using Kriging metamodels for robustness approximation in the CMA-ES","authors":"J. Kruisselbrink, M. Emmerich, A. Deutz, Thomas Bäck","doi":"10.1109/CEC.2010.5586235","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586235","url":null,"abstract":"This paper presents a study for using Kriging metamodeling in combination with Covariance Matrix Adaptation Evolution Strategies (CMA-ES) to find robust solutions. A general, archive based, framework is proposed for integrating Kriging within CMA-ES, including a method to utilize the covariance matrix of the CMA-ES in a straightforward way to improve the accuracy of the Kriging predictions without introducing much additional computational cost. Moreover, it adopts an elegant way to select appropriate archive points for building a local metamodel. The study shows that this Kriging metamodeling scheme for finding robust solutions outperforms common, straightforward approaches and is very useful when there is a limited budget of function evaluations. Though using the covariance matrix can improve the prediction quality, it has no significant effect on the overall quality of the optimization results.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78422050","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586201
P. Korošec, Katerina Tashkova, J. Silc
Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any optimization problem. In this paper, we address a performance evaluation of an ACO-based algorithm for solving large-scale global optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned to the graph vertices are used to navigate through the search space. The performance of the DASA is evaluated on the set of benchmark problems provided for CEC'2010 Special Session and Competition on Large-Scale Global Optimization.
{"title":"The differential Ant-Stigmergy Algorithm for large-scale global optimization","authors":"P. Korošec, Katerina Tashkova, J. Silc","doi":"10.1109/CEC.2010.5586201","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586201","url":null,"abstract":"Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any optimization problem. In this paper, we address a performance evaluation of an ACO-based algorithm for solving large-scale global optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA transforms a real-parameter optimization problem into a graph-search problem. The parameters' differences assigned to the graph vertices are used to navigate through the search space. The performance of the DASA is evaluated on the set of benchmark problems provided for CEC'2010 Special Session and Competition on Large-Scale Global Optimization.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75899146","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586417
Dong Wang, Hanwu Chen, Wanning Zhu
Quantum reversible logic circuits synthesis is one of the key technologies to construct quantum computer. The algebraic model for quantum information processing is a unitary matrix operator. Matrix can better reflect the quantum state evolution and the properties of quantum computation. Bidirectional matrix-based algorithm for quantum reversible logic circuits synthesis is proposed in this paper. The matrix representation of quantum reversible circuit and the circuit transformation rules of adjacent matrix are employed to construct any quantum reversible circuit in this paper. Compared with [11, 12], the computational complexity of our algorithm has been decreased exponentially and the speed has been increased by about 105 times. In addition, the types of the quantum reversible circuits synthesized by our algorithm are extended from only even permutations in [11, 12] to even and odd ones. we have synthesized 13!=6227020800 quantum reversible circuits, which can't be done by other algorithms.
{"title":"Bidirectional matrix-based algorithm for 4-qubit reversible logic circuits synthesis","authors":"Dong Wang, Hanwu Chen, Wanning Zhu","doi":"10.1109/CEC.2010.5586417","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586417","url":null,"abstract":"Quantum reversible logic circuits synthesis is one of the key technologies to construct quantum computer. The algebraic model for quantum information processing is a unitary matrix operator. Matrix can better reflect the quantum state evolution and the properties of quantum computation. Bidirectional matrix-based algorithm for quantum reversible logic circuits synthesis is proposed in this paper. The matrix representation of quantum reversible circuit and the circuit transformation rules of adjacent matrix are employed to construct any quantum reversible circuit in this paper. Compared with [11, 12], the computational complexity of our algorithm has been decreased exponentially and the speed has been increased by about 105 times. In addition, the types of the quantum reversible circuits synthesized by our algorithm are extended from only even permutations in [11, 12] to even and odd ones. we have synthesized 13!=6227020800 quantum reversible circuits, which can't be done by other algorithms.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72654557","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586352
Luis Martí, Jesús García, A. Berlanga, J. M. Molina
In this work we present a novel progress indicator, called fitness homogeneity indicator (FHI). This indicator improves the other previously discussed indicators as it takes into account all possible processes taking place in the population while not requiring an intensive computation as it relies on the fitness values calculated for the individuals. It is also capable of equally detecting success and failure scenarios, hopefully making an early detection of the second case.
{"title":"A progress indicator for detecting success and failure in evolutionary multi-objective optimization","authors":"Luis Martí, Jesús García, A. Berlanga, J. M. Molina","doi":"10.1109/CEC.2010.5586352","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586352","url":null,"abstract":"In this work we present a novel progress indicator, called fitness homogeneity indicator (FHI). This indicator improves the other previously discussed indicators as it takes into account all possible processes taking place in the population while not requiring an intensive computation as it relies on the fitness values calculated for the individuals. It is also capable of equally detecting success and failure scenarios, hopefully making an early detection of the second case.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79938531","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586430
Yan Chen, S. Mabu, K. Hirasawa
This paper proposes a new strategy β-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm (GRA). Since the portfolio beta β efficiently measures the volatility relative to the benchmark index or the capital market, β is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process. The main objective of this paper is to propose an integrated portfolio selection strategy, which selects stocks based on β using GRA. GRA is a new evolutionary algorithm designed to solve the optimization problem due to its special structure. We illustrate the proposed strategy by experiments and compare the results with those derived from the traditional models.
{"title":"A portfolio selection strategy using Genetic Relation Algorithm","authors":"Yan Chen, S. Mabu, K. Hirasawa","doi":"10.1109/CEC.2010.5586430","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586430","url":null,"abstract":"This paper proposes a new strategy β-GRA for portfolio selection in which the return and risk are considered as measures of strength in Genetic Relation Algorithm (GRA). Since the portfolio beta β efficiently measures the volatility relative to the benchmark index or the capital market, β is usually employed for portfolio evaluation or prediction, but scarcely for portfolio construction process. The main objective of this paper is to propose an integrated portfolio selection strategy, which selects stocks based on β using GRA. GRA is a new evolutionary algorithm designed to solve the optimization problem due to its special structure. We illustrate the proposed strategy by experiments and compare the results with those derived from the traditional models.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80162531","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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