Pub Date : 2003-12-08DOI: 10.1109/CEC.2003.1299832
A. Abraham, Vitorino Ramos
The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others [Ramos, V. et al. (2002), (2000)]. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly show that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when compared to evolutionary-fuzzy clustering (i-miner) [Abraham, A. (2003)] approach.
电子商务的快速发展使企业界和消费者都面临着新的形势。一方面，由于竞争激烈，客户有多种选择，企业界已经意识到智能营销策略和关系管理的必要性。Web使用挖掘试图从用户与Web的交互中获得的辅助数据中发现有用的知识。Web使用情况挖掘对于有效的Web站点管理、创建自适应Web站点、业务和支持服务、个性化、网络流量分析等已经变得非常关键。蚁群行为及其自组织能力的研究对知识检索/管理和决策支持系统科学很有兴趣，因为它提供了分布式自适应组织的模型，这对于解决困难的优化、分类和分布式控制问题等非常有用[Ramos, V. et al.(2002)，(2000)]。在本文中，我们提出了一种蚂蚁聚类算法来发现Web使用模式(数据簇)，并提出了一种线性遗传规划方法来分析访问者趋势。实证结果清楚地表明，蚁群聚类与自组织映射(用于聚类Web使用模式)相比表现良好，尽管与进化模糊聚类(i-miner)方法相比，性能准确性并不那么有效[Abraham, a .(2003)]。
{"title":"Web usage mining using artificial ant colony clustering and linear genetic programming","authors":"A. Abraham, Vitorino Ramos","doi":"10.1109/CEC.2003.1299832","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299832","url":null,"abstract":"The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. The study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others [Ramos, V. et al. (2002), (2000)]. In this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly show that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when compared to evolutionary-fuzzy clustering (i-miner) [Abraham, A. (2003)] approach.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125747423","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299821
Marcelo A. A. Araújo, E. Teixeira, Fábio R. Camargo, João P. V. Almeida
We present a peculiar parallel implementation of artificial neural networks using the backpropagation training algorithm. The message pass interface PVM is used in the Linux operating system environment, implemented in a cluster of IBM-PC machines. An optimized object-oriented framework to train neural networks, developed in C++, is part of the system presented. A shared memory framework was implemented to improve the training phase. One of the advantages of the system is the low cost, considering that its performance can be compared to similar powerful parallel machines.
{"title":"Parallel training for neural networks using PVM with shared memory","authors":"Marcelo A. A. Araújo, E. Teixeira, Fábio R. Camargo, João P. V. Almeida","doi":"10.1109/CEC.2003.1299821","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299821","url":null,"abstract":"We present a peculiar parallel implementation of artificial neural networks using the backpropagation training algorithm. The message pass interface PVM is used in the Linux operating system environment, implemented in a cluster of IBM-PC machines. An optimized object-oriented framework to train neural networks, developed in C++, is part of the system presented. A shared memory framework was implemented to improve the training phase. One of the advantages of the system is the low cost, considering that its performance can be compared to similar powerful parallel machines.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130124390","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299858
Andres Angantyr, Johan Andersson, J. Aidanpää
A criticism of evolutionary algorithms (EAs) might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods. EAs have received increased interest during the last decade due to the ease of handling multiple objectives. A constrained optimization problem or an unconstrained multiobjective problem may in principle be two different ways to pose the same underlying problem. In this paper, an alternative approach for the constrained optimization problem is presented. The method is a variant of a multiobjective real coded genetic algorithm (GA) inspired by the penalty approach. It is evaluated on six different constrained single objective problems found in the literature. The results show that the proposed method performs well in terms of efficiency, and that it is robust for a majority of the test problems.
{"title":"Constrained optimization based on a multiobjective evolutionary algorithm","authors":"Andres Angantyr, Johan Andersson, J. Aidanpää","doi":"10.1109/CEC.2003.1299858","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299858","url":null,"abstract":"A criticism of evolutionary algorithms (EAs) might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods. EAs have received increased interest during the last decade due to the ease of handling multiple objectives. A constrained optimization problem or an unconstrained multiobjective problem may in principle be two different ways to pose the same underlying problem. In this paper, an alternative approach for the constrained optimization problem is presented. The method is a variant of a multiobjective real coded genetic algorithm (GA) inspired by the penalty approach. It is evaluated on six different constrained single objective problems found in the literature. The results show that the proposed method performs well in terms of efficiency, and that it is robust for a majority of the test problems.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129467645","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299396
Grant Dick
Distributed population models improve the performance of genetic algorithms by assisting the selection scheme in maintaining diversity. A significant concern with these systems is that they need to be carefully configured in order to operate at their optimum. Failure to do so can often result in performance that is significantly under that of an equivalent panmitic implementation. We introduce a new distributed GA that requires little additional configuration over a panmitic GA. Early experimentation with this paradigm indicates that it is able to improve the searching abilities of the genetic algorithm on some problem domains.
{"title":"The spatially-dispersed genetic algorithm: an explicit spatial population structure for GAs","authors":"Grant Dick","doi":"10.1109/CEC.2003.1299396","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299396","url":null,"abstract":"Distributed population models improve the performance of genetic algorithms by assisting the selection scheme in maintaining diversity. A significant concern with these systems is that they need to be carefully configured in order to operate at their optimum. Failure to do so can often result in performance that is significantly under that of an equivalent panmitic implementation. We introduce a new distributed GA that requires little additional configuration over a panmitic GA. Early experimentation with this paradigm indicates that it is able to improve the searching abilities of the genetic algorithm on some problem domains.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128051167","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299550
E. Mezura-Montes, C. Coello
In this paper, we propose the use of a simple evolution strategy (SES) (i.e., a (1 + /spl lambda/)-ES with self-adaptation that uses three tournament rules based on feasibility) coupled with a diversity mechanism to solve constrained optimization problems. The proposed mechanism is based on multiobjective optimization concepts taken from an approach called the niched-Pareto genetic algorithm (NPGA). The main advantage of the proposed approach is that it does not require the definition of any extra parameters, other than those required by an evolution strategy. The performance of the proposed approach is shown to be highly competitive with respect to other constraint-handling techniques representative of the state-of-the-art in the area when using a set of well-known benchmarks.
{"title":"Adding a diversity mechanism to a simple evolution strategy to solve constrained optimization problems","authors":"E. Mezura-Montes, C. Coello","doi":"10.1109/CEC.2003.1299550","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299550","url":null,"abstract":"In this paper, we propose the use of a simple evolution strategy (SES) (i.e., a (1 + /spl lambda/)-ES with self-adaptation that uses three tournament rules based on feasibility) coupled with a diversity mechanism to solve constrained optimization problems. The proposed mechanism is based on multiobjective optimization concepts taken from an approach called the niched-Pareto genetic algorithm (NPGA). The main advantage of the proposed approach is that it does not require the definition of any extra parameters, other than those required by an evolution strategy. The performance of the proposed approach is shown to be highly competitive with respect to other constraint-handling techniques representative of the state-of-the-art in the area when using a set of well-known benchmarks.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124791448","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299436
K. Tagawa, Tetsuya Yamamoto, T. Igaki, S. Seki
The frequency response characteristics of surface acoustic wave (SAW) filters are governed primarily by their geometrical structures, i.e., the configurations of interdigital transducers (IDTs) and reflectors arranged on piezoelectric substrates. We present an Imanishian genetic algorithm (GA), which is based on an evolutionary theory advocated by a Japanese ecologist, Kinji Imanishi, for the structural design of SAW filters. In the proposed Imanishian GA, each species is discriminated from others according to the distance between individuals. Then, the generation model tries to hold various species in the population as many as possible. In addition, a local search is used to improve respective individuals effectively. As a result, in comparison with traditional Darwinian GAs, the Imanishian GA is better at taking balance between exploration and exploitation. Computational experiments conducted on an optimum design of a resonator type SAW filter demonstrate the usefulness of the Imanishian GA.
{"title":"An Imanishian genetic algorithm for the optimum design of surface acoustic wave filter","authors":"K. Tagawa, Tetsuya Yamamoto, T. Igaki, S. Seki","doi":"10.1109/CEC.2003.1299436","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299436","url":null,"abstract":"The frequency response characteristics of surface acoustic wave (SAW) filters are governed primarily by their geometrical structures, i.e., the configurations of interdigital transducers (IDTs) and reflectors arranged on piezoelectric substrates. We present an Imanishian genetic algorithm (GA), which is based on an evolutionary theory advocated by a Japanese ecologist, Kinji Imanishi, for the structural design of SAW filters. In the proposed Imanishian GA, each species is discriminated from others according to the distance between individuals. Then, the generation model tries to hold various species in the population as many as possible. In addition, a local search is used to improve respective individuals effectively. As a result, in comparison with traditional Darwinian GAs, the Imanishian GA is better at taking balance between exploration and exploitation. Computational experiments conducted on an optimum design of a resonator type SAW filter demonstrate the usefulness of the Imanishian GA.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131111447","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299898
Y. Ong, K. Lum, P. Nair, Daming Shi, Z. Zhang
In this paper, we present an evolutionary framework for efficient aerodynamic shape design. The approach suggests employing hybrid evolutionary algorithm with gradient-based local search method in the spirit of Lamarckian and surrogate models that approximates the computationally expensive adjoint computational fluid dynamics during design search. In particular, we reveal that the proposed framework guarantees global convergence by inheriting the properties of trust-region method to interleave use of the exact solver for the objective function with computationally cheap surrogate models during local search. Empirical results on 2D airfoil shape design using an adjoint inverse pressure design problem indicates that the approaches global convergences on a limited computational budget.
{"title":"Global convergence of unconstrained and bound constrained surrogate-assisted evolutionary search in aerodynamic shape design","authors":"Y. Ong, K. Lum, P. Nair, Daming Shi, Z. Zhang","doi":"10.1109/CEC.2003.1299898","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299898","url":null,"abstract":"In this paper, we present an evolutionary framework for efficient aerodynamic shape design. The approach suggests employing hybrid evolutionary algorithm with gradient-based local search method in the spirit of Lamarckian and surrogate models that approximates the computationally expensive adjoint computational fluid dynamics during design search. In particular, we reveal that the proposed framework guarantees global convergence by inheriting the properties of trust-region method to interleave use of the exact solver for the objective function with computationally cheap surrogate models during local search. Empirical results on 2D airfoil shape design using an adjoint inverse pressure design problem indicates that the approaches global convergences on a limited computational budget.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130058641","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299438
Gaofeng Huang, A. Lim
Three-index assignment problem (AP3) is well-known problem which has been shown to be NP-hard. This problem has been studied extensively, and many exact and heuristic methods have been proposed to solve it. Inspired by the classical assignment problem, we propose a new iterative heuristic, called fragmental optimization (FO), which solves the problem by simplifying it to the assignment problem. We further hybridize our heuristic with the genetic algorithm (GA). Extensive experimental results indicate that our hybrid method to be superior to all previous heuristic methods including those proposed by Balas and Saltzman(1991), Crama and Spieksma(1992), Burkard et al(1996), and Aiex et al(2003).
{"title":"A hybrid genetic algorithm for three-index assignment problem","authors":"Gaofeng Huang, A. Lim","doi":"10.1109/CEC.2003.1299438","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299438","url":null,"abstract":"Three-index assignment problem (AP3) is well-known problem which has been shown to be NP-hard. This problem has been studied extensively, and many exact and heuristic methods have been proposed to solve it. Inspired by the classical assignment problem, we propose a new iterative heuristic, called fragmental optimization (FO), which solves the problem by simplifying it to the assignment problem. We further hybridize our heuristic with the genetic algorithm (GA). Extensive experimental results indicate that our hybrid method to be superior to all previous heuristic methods including those proposed by Balas and Saltzman(1991), Crama and Spieksma(1992), Burkard et al(1996), and Aiex et al(2003).","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669965","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299796
G. Parker, Andrey S. Anev, Dejan Duzevik
We describe a system that uses evolutionary computation to evolve tower-like structures. The construction takes place in a computer simulated gravitational environment. The evolution targets the morphology; each chromosome carries structural description of the entity. Fitness functions evaluate the structural integrity and "goodness" of each individual based on indicators such as joint tension, center of gravity, position in space, height, etc. Twelve evolution-tests were performed and all successfully reached tower solutions.
{"title":"Evolving towers in a 3-dimensional simulated environment","authors":"G. Parker, Andrey S. Anev, Dejan Duzevik","doi":"10.1109/CEC.2003.1299796","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299796","url":null,"abstract":"We describe a system that uses evolutionary computation to evolve tower-like structures. The construction takes place in a computer simulated gravitational environment. The evolution targets the morphology; each chromosome carries structural description of the entity. Fitness functions evaluate the structural integrity and \"goodness\" of each individual based on indicators such as joint tension, center of gravity, position in space, height, etc. Twelve evolution-tests were performed and all successfully reached tower solutions.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121832756","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299928
Hussein A. Abbass
In this paper, we present a comparison between two multiobjective formulations to the formation of neuro-ensembles. The first formulation splits the training set into two nonoverlapping stratified subsets and form an objective to minimize the training error on each subset, while the second formulation adds random noise to the training set to form a second objective. A variation of the memetic Pareto artificial neural network (MPANN) algorithm is used. MPANN is based on differential evolution for continuous optimization. The ensemble is formed from all networks on the Pareto frontier. It is found that the first formulation outperformed the second. The first formulation is also found to be competitive to other methods in the literature.
{"title":"Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization","authors":"Hussein A. Abbass","doi":"10.1109/CEC.2003.1299928","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299928","url":null,"abstract":"In this paper, we present a comparison between two multiobjective formulations to the formation of neuro-ensembles. The first formulation splits the training set into two nonoverlapping stratified subsets and form an objective to minimize the training error on each subset, while the second formulation adds random noise to the training set to form a second objective. A variation of the memetic Pareto artificial neural network (MPANN) algorithm is used. MPANN is based on differential evolution for continuous optimization. The ensemble is formed from all networks on the Pareto frontier. It is found that the first formulation outperformed the second. The first formulation is also found to be competitive to other methods in the literature.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121984794","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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