Pub Date : 2014-12-08DOI: 10.1109/ICNC.2014.6975917
Vaishali S. Pawar, M. Zaveri
The graph is an efficient data structure to represent multi-dimensional data and their complex relations. Pattern matching and data mining are the two important fields of computer science. Pattern matching finds a particular pattern in the given input where as data mining deals with selecting specific data from the huge databases. This work contributes towards the combination of graph theory, pattern recognition and graph based databases. A variety of graph based techniques have been proposed as a powerful tool for pattern representation and classification in the past years. For a longer time graphs remained computationally expensive tool. But recently the graph based structural pattern recognition and image processing is becoming popular. The computational complexity of the graph based methods is becoming feasible due to high end new generations of the computers and the research advancements. In this work we have implemented graph based fingerprint recognition algorithm. The fingerprints are represented as attributed relational graphs. In the pattern recognition phase graph matching is applied. This study focuses on the clustering of graph databases prior to graph matching. When the structural feature set size of the data grows longer, graph matching becomes expensive. The clustering of graph databases drastically reduce the graph matching candidates.
{"title":"Graph based K-nearest neighbor minutiae clustering for fingerprint recognition","authors":"Vaishali S. Pawar, M. Zaveri","doi":"10.1109/ICNC.2014.6975917","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975917","url":null,"abstract":"The graph is an efficient data structure to represent multi-dimensional data and their complex relations. Pattern matching and data mining are the two important fields of computer science. Pattern matching finds a particular pattern in the given input where as data mining deals with selecting specific data from the huge databases. This work contributes towards the combination of graph theory, pattern recognition and graph based databases. A variety of graph based techniques have been proposed as a powerful tool for pattern representation and classification in the past years. For a longer time graphs remained computationally expensive tool. But recently the graph based structural pattern recognition and image processing is becoming popular. The computational complexity of the graph based methods is becoming feasible due to high end new generations of the computers and the research advancements. In this work we have implemented graph based fingerprint recognition algorithm. The fingerprints are represented as attributed relational graphs. In the pattern recognition phase graph matching is applied. This study focuses on the clustering of graph databases prior to graph matching. When the structural feature set size of the data grows longer, graph matching becomes expensive. The clustering of graph databases drastically reduce the graph matching candidates.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"7 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114019869","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975880
A. Wedyan, A. Narayanan
The Capacitated Vehicle Routing Problem (CVRP) is a well-known NP-hard problem of importance to real life applications such as transportation and logistics. The CVRP finds the best set of paths between a specific number of customers to deliver goods by a number of vehicles with a fixed capacity. In this paper, we apply a new nature inspired optimization algorithm called Intelligent Water Drops (IWD) inspired by water flow. The results of this IWD approach are compared against a classical approach and show that IWD algorithm gives optimal and near optimal solutions for some CVRP instances.
{"title":"Solving capacitated vehicle routing problem using intelligent water drops algorithm","authors":"A. Wedyan, A. Narayanan","doi":"10.1109/ICNC.2014.6975880","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975880","url":null,"abstract":"The Capacitated Vehicle Routing Problem (CVRP) is a well-known NP-hard problem of importance to real life applications such as transportation and logistics. The CVRP finds the best set of paths between a specific number of customers to deliver goods by a number of vehicles with a fixed capacity. In this paper, we apply a new nature inspired optimization algorithm called Intelligent Water Drops (IWD) inspired by water flow. The results of this IWD approach are compared against a classical approach and show that IWD algorithm gives optimal and near optimal solutions for some CVRP instances.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117061808","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975950
Qiaoyong Zhong, D. Niedieker, Dennis Petersen, K. Gerwert, A. Mosig
Recent approaches to multispectral microscopy such as infrared, Raman and CARS microscopy produce large amounts of high-dimensional spectra at high spatial resolution. In this context, we propose and validate a method for unsupervised feature selection. Unsupervised feature selection is of relevance in several applications of multispectral imaging techniques, most notably in reducing the measurement time of CARS microscopic experiments. Our feature selection is based on minimizing a mutual-information based measure of redundancy, and can be seen as the unsupervised version of the well established minimal-redundancy-maximal-relevance approach to supervised feature selection. We compare our approach to previously proposed unsupervised feature selection approaches and demonstrate its advantages on two types of multispectral imaging techniques as well as on synthetic data.
{"title":"Identifying minimally redundant wavenumbers for vibrational microspectroscopic image analysis","authors":"Qiaoyong Zhong, D. Niedieker, Dennis Petersen, K. Gerwert, A. Mosig","doi":"10.1109/ICNC.2014.6975950","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975950","url":null,"abstract":"Recent approaches to multispectral microscopy such as infrared, Raman and CARS microscopy produce large amounts of high-dimensional spectra at high spatial resolution. In this context, we propose and validate a method for unsupervised feature selection. Unsupervised feature selection is of relevance in several applications of multispectral imaging techniques, most notably in reducing the measurement time of CARS microscopic experiments. Our feature selection is based on minimizing a mutual-information based measure of redundancy, and can be seen as the unsupervised version of the well established minimal-redundancy-maximal-relevance approach to supervised feature selection. We compare our approach to previously proposed unsupervised feature selection approaches and demonstrate its advantages on two types of multispectral imaging techniques as well as on synthetic data.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"1652 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115839649","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975833
D. U. Wutsqa, R. Kusumawati, R. Subekti
Recurrent neural network is a network which provides feedback connections. This network is believed to have a more powerful approach than the typical neural network for learning given data. The current research was aimed to apply the simplest recurrent neural network model, namely the Elman recurrent neural network (ERNN) model, to the consumer price index (CPI) of education, recreation, and sports data in Yogyakarta. The pattern of CPI data can be categorized as a function of time period, which tends to move upwards when the time period is increased, and jump at some points of the time period. This pattern was identified as similar to the pattern resulted by the function of the truncated polynomial spline regression model (TPSR). Hence, this research considered ERNN model which the inputs such as in the TPSR model were established by taking into account the location of the knot or jump points. In addition, the ERNN model with a single input, a time period was also generated. The results demonstrated that the two models have high accuracy both in training and testing data. More importantly, it was found that the first model is more appropriate than the second one in testing data.
{"title":"The application of Elman recurrent neural network model for forecasting consumer price index of education, recreation and sports in Yogyakarta","authors":"D. U. Wutsqa, R. Kusumawati, R. Subekti","doi":"10.1109/ICNC.2014.6975833","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975833","url":null,"abstract":"Recurrent neural network is a network which provides feedback connections. This network is believed to have a more powerful approach than the typical neural network for learning given data. The current research was aimed to apply the simplest recurrent neural network model, namely the Elman recurrent neural network (ERNN) model, to the consumer price index (CPI) of education, recreation, and sports data in Yogyakarta. The pattern of CPI data can be categorized as a function of time period, which tends to move upwards when the time period is increased, and jump at some points of the time period. This pattern was identified as similar to the pattern resulted by the function of the truncated polynomial spline regression model (TPSR). Hence, this research considered ERNN model which the inputs such as in the TPSR model were established by taking into account the location of the knot or jump points. In addition, the ERNN model with a single input, a time period was also generated. The results demonstrated that the two models have high accuracy both in training and testing data. More importantly, it was found that the first model is more appropriate than the second one in testing data.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123123959","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975834
Leiyue Yao, D. Xu, Weijian Zhang, Jie Zhou
According to the specific business requirements of the meteorological wireless sensor network (MWSN), we analyze and summarize the principles and problems of the existing Web real-time communication technology, and put forward the real-time meteorological information publishing platform (RT-MIPP) solution based on WebSocket. We first analyze and classify the observational data of MWSN, and design the meteorological information packet format. On this basis, the overall framework design of middleware server (MWServer) is established, the functions of each module are described in detail, and the specific design of MWServer software platform is given. At the same time, through the comparison of all kinds of real-time Web communication technology, the WebSocket significant advantages in low-latency and low network throughput is obtained, and the information Web publishing platform is designed. Actual operating results show that, using .NET and WebSocket technology, greatly improve the performance of system in realtime, versatility and flexibility, simplify the process of later modifications and maintenance, and achieve the expected effect of the authors.
{"title":"Using WebSocket-based technology to build real-time meteorological wireless sensor network information publishing platform","authors":"Leiyue Yao, D. Xu, Weijian Zhang, Jie Zhou","doi":"10.1109/ICNC.2014.6975834","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975834","url":null,"abstract":"According to the specific business requirements of the meteorological wireless sensor network (MWSN), we analyze and summarize the principles and problems of the existing Web real-time communication technology, and put forward the real-time meteorological information publishing platform (RT-MIPP) solution based on WebSocket. We first analyze and classify the observational data of MWSN, and design the meteorological information packet format. On this basis, the overall framework design of middleware server (MWServer) is established, the functions of each module are described in detail, and the specific design of MWServer software platform is given. At the same time, through the comparison of all kinds of real-time Web communication technology, the WebSocket significant advantages in low-latency and low network throughput is obtained, and the information Web publishing platform is designed. Actual operating results show that, using .NET and WebSocket technology, greatly improve the performance of system in realtime, versatility and flexibility, simplify the process of later modifications and maintenance, and achieve the expected effect of the authors.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123556571","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975932
Zuoyong Xiang, Zhenghong Yu
This paper presents a clustering algorithm based on Voronoi diagrams. The algorithm firstly constructs irregular grids in plane by Voronoi diagrams, then assign the points among different grids to different clusters according to the property of the Voronoi diagrams' “the nearest neighbor”. It is able to automatically modify the final clustering number based on the grid points' density, and it can adjust the locations for the Voronoi's seeds by the changes of the centroids, and the final Voronoi cells becomes the clustering result. The algorithm is able to settle down the clustering numbers automatically and also can recognize the low density points automatically. The experiments prove that the algorithm can cluster effectively the data points in plane, and its performance is similar to the X-means algorithm which is improved on the K-means algorithm. It is more effective than the DBSCAN and the OPTICS which are density-based clustering algorithms. The algorithm proved to be obviously more effective while the experimental data is in a larger scale.
{"title":"Voronoi-clustering for plane data","authors":"Zuoyong Xiang, Zhenghong Yu","doi":"10.1109/ICNC.2014.6975932","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975932","url":null,"abstract":"This paper presents a clustering algorithm based on Voronoi diagrams. The algorithm firstly constructs irregular grids in plane by Voronoi diagrams, then assign the points among different grids to different clusters according to the property of the Voronoi diagrams' “the nearest neighbor”. It is able to automatically modify the final clustering number based on the grid points' density, and it can adjust the locations for the Voronoi's seeds by the changes of the centroids, and the final Voronoi cells becomes the clustering result. The algorithm is able to settle down the clustering numbers automatically and also can recognize the low density points automatically. The experiments prove that the algorithm can cluster effectively the data points in plane, and its performance is similar to the X-means algorithm which is improved on the K-means algorithm. It is more effective than the DBSCAN and the OPTICS which are density-based clustering algorithms. The algorithm proved to be obviously more effective while the experimental data is in a larger scale.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"430 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122801267","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975838
Feng Wang, Yuan Man, Lichun Man
To address the k shortest paths (KSP) problem, an intelligent optimization approach based on Genetic Algorithm (GA) is presented in this paper. A simple and intuitive natural path representation is firstly employed to be the chromosome encoding scheme. Then genetic operators specific to this encoding scheme are defined respectively. Each partial route of two chosen chromosomes is exchanged by a one-point crossover operator at common intersections. A one and two-point mutation operators are adopted to perform mutation operations for directed and undirected graphs respectively. And a bidirectional searching strategy is applied to eliminate loops in the paths generated by the above genetic operators. Comparative experiments were conducted on test graphs by using different strategies of genetic operations, mutation rates and operators. And the experimental results verify the validity of the proposed algorithm.
{"title":"Intelligent optimization approach for the k shortest paths problem based on genetic algorithm","authors":"Feng Wang, Yuan Man, Lichun Man","doi":"10.1109/ICNC.2014.6975838","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975838","url":null,"abstract":"To address the k shortest paths (KSP) problem, an intelligent optimization approach based on Genetic Algorithm (GA) is presented in this paper. A simple and intuitive natural path representation is firstly employed to be the chromosome encoding scheme. Then genetic operators specific to this encoding scheme are defined respectively. Each partial route of two chosen chromosomes is exchanged by a one-point crossover operator at common intersections. A one and two-point mutation operators are adopted to perform mutation operations for directed and undirected graphs respectively. And a bidirectional searching strategy is applied to eliminate loops in the paths generated by the above genetic operators. Comparative experiments were conducted on test graphs by using different strategies of genetic operations, mutation rates and operators. And the experimental results verify the validity of the proposed algorithm.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123920328","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975844
H. Ge, Z. Ma, Liang Sun
In this paper, a Hierarchical Particle Swarm Optimizer with Random Social Cognition, briefly expressed as HPSO-RSC, is proposed. During the execution process of HPSO-RSC, the social environment is changed dynamically, and each particle is not only attracted by its previous best particle and the global best particle of the whole population, but also attracted by all other better particles randomly. During the early stage of the execution process, to speed up convergence of the algorithm, the particles are inclined to choose the global best particle as cognition object. On the other hand, during the late stage of the execution process, to keep the diversity of the population, the particles are inclined to choose the particles that better than themselves as cognition object. To solve the large scale global optimization problem, the algorithm is integrated into a cooperative coevolution framework with an efficient variable interaction checking method. Simulated experiments were conducted on the CEC'2008 benchmarks. The result demonstrates that, HPSO-RSC has strong ability to find the global optimum for most of the benchmark problems.
{"title":"A hierarchical particle swarm optimizer with random social cognition for large scale global optimization","authors":"H. Ge, Z. Ma, Liang Sun","doi":"10.1109/ICNC.2014.6975844","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975844","url":null,"abstract":"In this paper, a Hierarchical Particle Swarm Optimizer with Random Social Cognition, briefly expressed as HPSO-RSC, is proposed. During the execution process of HPSO-RSC, the social environment is changed dynamically, and each particle is not only attracted by its previous best particle and the global best particle of the whole population, but also attracted by all other better particles randomly. During the early stage of the execution process, to speed up convergence of the algorithm, the particles are inclined to choose the global best particle as cognition object. On the other hand, during the late stage of the execution process, to keep the diversity of the population, the particles are inclined to choose the particles that better than themselves as cognition object. To solve the large scale global optimization problem, the algorithm is integrated into a cooperative coevolution framework with an efficient variable interaction checking method. Simulated experiments were conducted on the CEC'2008 benchmarks. The result demonstrates that, HPSO-RSC has strong ability to find the global optimum for most of the benchmark problems.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125572739","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 : 2014-12-08DOI: 10.1109/ICNC.2014.6975842
B. Thabti, H. Youssef, A. Mahjoub, A. Meddeb
In this paper, we studied Virtual Private Network Design Problem using tree structure and assuming a pipe traffic matrix. Today with network virtualization, Virtual Private Networks (VPNs) have more importance in networking and offers the company the ideal solution to establish ondemand overlay networks that enable their customers to securely access company resources. This is a hard combinatorial optimization problem that it has been solved in literature only with approximation methods. Generally, this kind of methods do not give any guarantee on the solution quality and we are enable to know a priori how far the given solution is from the optimal one unlike other methods such as the exact methods. For this purpose, we propose an integer linear program (ILP) with classical Pipe traffic model to design the problem under investigation. Based on the proposed integer programming formulation, we solve the problem using two approaches: The first contribution is Simulated Evolution based evolutionary algorithm and the second contribution is an exact method based on Branch and Cut (B&C) algorithm to find a tree rooted at a user specified node with minimized overall reserved bandwidth. Performance results using Brite networks show that our proposed evolutionary algorithm offers good solutions within a fraction of the time required by the B&C algorithm and bandwidth cost within at most 1.5% of the optimal solutions found by the exact method.
本文采用树形结构，假设一个管道流量矩阵，研究了虚拟专用网的设计问题。在网络虚拟化的今天，虚拟专用网(vpn)在网络中更加重要，并为公司提供了理想的解决方案，以建立按需覆盖网络，使其客户能够安全地访问公司资源。这是一个很难的组合优化问题，在文献中只能用近似方法来解决。一般来说，这种方法不保证解的质量，而且与其他方法如精确方法不同，我们可以先验地知道给定解与最优解的距离。为此，我们提出了一个基于经典管道交通模型的整数线性规划(ILP)来设计所研究的问题。基于所提出的整数规划公式，我们采用两种方法来解决问题:第一种是基于模拟进化的进化算法，第二种是基于Branch and Cut (B&C)算法的精确方法，以最小的总体保留带宽在用户指定节点上找到扎根的树。使用Brite网络的性能结果表明，我们提出的进化算法在B&C算法所需的时间的一小部分内提供了良好的解，并且带宽成本最多在精确方法找到的最优解的1.5%内。
{"title":"Simulated evolution based algorithm versus exact method for virtual private network design","authors":"B. Thabti, H. Youssef, A. Mahjoub, A. Meddeb","doi":"10.1109/ICNC.2014.6975842","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975842","url":null,"abstract":"In this paper, we studied Virtual Private Network Design Problem using tree structure and assuming a pipe traffic matrix. Today with network virtualization, Virtual Private Networks (VPNs) have more importance in networking and offers the company the ideal solution to establish ondemand overlay networks that enable their customers to securely access company resources. This is a hard combinatorial optimization problem that it has been solved in literature only with approximation methods. Generally, this kind of methods do not give any guarantee on the solution quality and we are enable to know a priori how far the given solution is from the optimal one unlike other methods such as the exact methods. For this purpose, we propose an integer linear program (ILP) with classical Pipe traffic model to design the problem under investigation. Based on the proposed integer programming formulation, we solve the problem using two approaches: The first contribution is Simulated Evolution based evolutionary algorithm and the second contribution is an exact method based on Branch and Cut (B&C) algorithm to find a tree rooted at a user specified node with minimized overall reserved bandwidth. Performance results using Brite networks show that our proposed evolutionary algorithm offers good solutions within a fraction of the time required by the B&C algorithm and bandwidth cost within at most 1.5% of the optimal solutions found by the exact method.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129259292","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}
The genetic algorithm is widely applied to all kinds of formula problems for its characteristics of simpleness, universality, strong robustness and less mathematical demands for optimization problems. However, the traditional standard genetic algorithm has a great blindness when generating the initial population and in the crossover and mutation process, which results in extremely low efficiency. In this paper, according to the characteristics of the formula problems, we propose to add constraints of formula problems to the initial population generation process and the crossover and mutation process and this reduces the blindness and improves the algorithm efficiency. In view of recipe issues, a quick generation method for the initial population is presented and a new crossover and mutation method is presented. We implemented the optimized genetic algorithm on Matlab and verified the feasibility and high-efficiency of the algorithm.
{"title":"The application and optimization of genetic algorithms in formula problems","authors":"Nian-yun Shi, Pei-yao Li, Zhuo-jun Li, Qing-dong Zhang","doi":"10.1109/ICNC.2014.6975843","DOIUrl":"https://doi.org/10.1109/ICNC.2014.6975843","url":null,"abstract":"The genetic algorithm is widely applied to all kinds of formula problems for its characteristics of simpleness, universality, strong robustness and less mathematical demands for optimization problems. However, the traditional standard genetic algorithm has a great blindness when generating the initial population and in the crossover and mutation process, which results in extremely low efficiency. In this paper, according to the characteristics of the formula problems, we propose to add constraints of formula problems to the initial population generation process and the crossover and mutation process and this reduces the blindness and improves the algorithm efficiency. In view of recipe issues, a quick generation method for the initial population is presented and a new crossover and mutation method is presented. We implemented the optimized genetic algorithm on Matlab and verified the feasibility and high-efficiency of the algorithm.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128241269","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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