Pub Date : 2002-05-12DOI: 10.1109/CEC.2002.1006272
R. Kumar, P. P. Parida, Mohit Gupta
Designing communication networks is a complex, multi-constraint and multi-criterion optimization problem. We present a multi-objective genetic optimization approach to setting up a network while simultaneously minimizing network delays and installation costs subject to reliability and flow constraints. In this paper, we use a Pareto-converging genetic algorithm, present results for two test networks and compare results with another heuristic method.
{"title":"Topological design of communication networks using multiobjective genetic optimization","authors":"R. Kumar, P. P. Parida, Mohit Gupta","doi":"10.1109/CEC.2002.1006272","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006272","url":null,"abstract":"Designing communication networks is a complex, multi-constraint and multi-criterion optimization problem. We present a multi-objective genetic optimization approach to setting up a network while simultaneously minimizing network delays and installation costs subject to reliability and flow constraints. In this paper, we use a Pareto-converging genetic algorithm, present results for two test networks and compare results with another heuristic method.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128823092","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 : 2002-05-12DOI: 10.1109/CEC.2002.1006996
M. A. Guinea, G. Gutiérrez, I. Galván, A. Sanchis, J. M. Molina
Designing the optimal neural net (NN) architecture can be formulated as a search problem in the architectures space, where each point represents an architecture. The search space of all possible architectures is very large, and the task of finding the simplest architecture may be an arduous and mostly a random task. Methods based on indirect encoding have been used to reduce the chromosome length. In this paper, a new indirect encoding method is proposed and an analysis of the generative capacity of the method is presented.
{"title":"Generative capacities of grammars codification for evolution of NN architectures","authors":"M. A. Guinea, G. Gutiérrez, I. Galván, A. Sanchis, J. M. Molina","doi":"10.1109/CEC.2002.1006996","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006996","url":null,"abstract":"Designing the optimal neural net (NN) architecture can be formulated as a search problem in the architectures space, where each point represents an architecture. The search space of all possible architectures is very large, and the task of finding the simplest architecture may be an arduous and mostly a random task. Methods based on indirect encoding have been used to reduce the chromosome length. In this paper, a new indirect encoding method is proposed and an analysis of the generative capacity of the method is presented.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129383853","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004421
E. Avgerinos, A. Zalzala, G. Zografos
The main objective of this paper is to use genetic algorithms in order to improve the quality of the bathymetry derived from sidescan raw data. The optimisation sequence starts with inverse modelling of the phase data, which uniquely corresponds to the characteristics of the coupled system of the sidescan vehicle and the seafloor terrain. These phase data are then compared with phase data actually collected by the sonar, to produce a correlation coefficient as an objective function. Simulation results are reported for the algorithm showing robust convergence towards the optimum value of the objective function. The results indicate that this new approach can be used to avoid difficulties widely encountered during forward processing of phase data to derive bathymetry.
{"title":"Towards evolutionary optimisation for high resolution bathymetry from sidescan sonars","authors":"E. Avgerinos, A. Zalzala, G. Zografos","doi":"10.1109/CEC.2002.1004421","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004421","url":null,"abstract":"The main objective of this paper is to use genetic algorithms in order to improve the quality of the bathymetry derived from sidescan raw data. The optimisation sequence starts with inverse modelling of the phase data, which uniquely corresponds to the characteristics of the coupled system of the sidescan vehicle and the seafloor terrain. These phase data are then compared with phase data actually collected by the sonar, to produce a correlation coefficient as an objective function. Simulation results are reported for the algorithm showing robust convergence towards the optimum value of the objective function. The results indicate that this new approach can be used to avoid difficulties widely encountered during forward processing of phase data to derive bathymetry.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116828757","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004438
Kosuke Yamamoto, T. Yoshikawa, T. Furuhashi, T. Shinogi, S. Tsuruoka
The search performance of evolutionary algorithms (EAs) has been widely studied. Interactions between genes in a chromosome, called "epistasis", make the theoretical investigation difficult. The goal of this study is a mathematical analysis of the effects of bacterial mutation on a bacterial evolutionary algorithm (BEA). The NK-landscape problem is employed for the investigation of this analysis in this paper. The search ability of bacterial mutation is formulated and compared with those of conventional mutation operations. It is shown that the bacterial mutation surpasses conventional ones in search performance.
{"title":"Evaluation of search performance of bacterial evolutionary algorithm","authors":"Kosuke Yamamoto, T. Yoshikawa, T. Furuhashi, T. Shinogi, S. Tsuruoka","doi":"10.1109/CEC.2002.1004438","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004438","url":null,"abstract":"The search performance of evolutionary algorithms (EAs) has been widely studied. Interactions between genes in a chromosome, called \"epistasis\", make the theoretical investigation difficult. The goal of this study is a mathematical analysis of the effects of bacterial mutation on a bacterial evolutionary algorithm (BEA). The NK-landscape problem is employed for the investigation of this analysis in this paper. The search ability of bacterial mutation is formulated and compared with those of conventional mutation operations. It is shown that the bacterial mutation surpasses conventional ones in search performance.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117020502","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004491
Jih Tsung Yang, Hsien-Da Huang, Jorng-Tzong Horng
We discuss the scheduling problems of a sports league and propose a new approach to solve these problems by applying evolution strategy. A schedule in a sports league must satisfy many constraints on timing, such as the number of games played between every pair of teams, the bounds on the number of consecutive home (or away) games for each team, every pair of teams must have played each other in the first half of the season, and so on. In addition to finding a feasible schedule that meets all the timing restrictions, the problem addressed has the additional complexity of having the objective of minimizing travel costs and every team having a balanced number of games at home. We formalize the scheduling problem into an optimization problem and adopt the concept of evolution strategy to solve it. We define the travel cost and distance cost for teams in the sports league by referring to Major League Baseball (MLB) in the United States and focus on the scheduling problem in MLB. Using the new method, it is more efficient at finding better results than previous approaches.
{"title":"Devising a cost effective baseball scheduling by evolutionary algorithms","authors":"Jih Tsung Yang, Hsien-Da Huang, Jorng-Tzong Horng","doi":"10.1109/CEC.2002.1004491","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004491","url":null,"abstract":"We discuss the scheduling problems of a sports league and propose a new approach to solve these problems by applying evolution strategy. A schedule in a sports league must satisfy many constraints on timing, such as the number of games played between every pair of teams, the bounds on the number of consecutive home (or away) games for each team, every pair of teams must have played each other in the first half of the season, and so on. In addition to finding a feasible schedule that meets all the timing restrictions, the problem addressed has the additional complexity of having the objective of minimizing travel costs and every team having a balanced number of games at home. We formalize the scheduling problem into an optimization problem and adopt the concept of evolution strategy to solve it. We define the travel cost and distance cost for teams in the sports league by referring to Major League Baseball (MLB) in the United States and focus on the scheduling problem in MLB. Using the new method, it is more efficient at finding better results than previous approaches.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117132647","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004432
H. Ghezelayagh, Kwang Y. Lee
An intelligent predictive controller is implemented to control a fossil fuel power unit. This controller is a non-model based system that uses a self-organized neuro-fuzzy identifier to predict the response of the plant in a future time interval. The control inputs are optimized in this prediction horizon by evolutionary programming (EP) to minimize the error of identifier outputs and reference set points. The identifier performs automatic rule generation and membership function tuning by genetic algorithm (GA) and error back-propagation methods, respectively. This intelligent system provides a predictive control of multi-input multi-output nonlinear systems with slow time variation.
{"title":"Intelligent predictive control of a power plant with evolutionary programming optimizer and neuro-fuzzy identifier","authors":"H. Ghezelayagh, Kwang Y. Lee","doi":"10.1109/CEC.2002.1004432","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004432","url":null,"abstract":"An intelligent predictive controller is implemented to control a fossil fuel power unit. This controller is a non-model based system that uses a self-organized neuro-fuzzy identifier to predict the response of the plant in a future time interval. The control inputs are optimized in this prediction horizon by evolutionary programming (EP) to minimize the error of identifier outputs and reference set points. The identifier performs automatic rule generation and membership function tuning by genetic algorithm (GA) and error back-propagation methods, respectively. This intelligent system provides a predictive control of multi-input multi-output nonlinear systems with slow time variation.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182983","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 : 2002-05-12DOI: 10.1109/CEC.2002.1007062
Yong Zhang, L. Hall, Dmitry Goldgof, S. Sarkar
This paper presents a constrained genetic approach (CGA) for reconstructing the Young's modulus of elastic objects. Qualitative a priori information is incorporated using a rank based scheme to constrain the admissible solutions. Balance between the fitness function (adhesion to the measurement data) and the penalty function (fidelity to a priori knowledge) is achieved by a stochastic sort algorithm. The over-smoothing of Young's modulus discontinuity is avoided without the need of computing a deterministic weight coefficient. The experiment on synthetic data indicates that the proposed method not only reconstructed reliable Young's modulus from noisy data, but also expedited the convergence process significantly.
{"title":"A constrained genetic approach for reconstructing Young's modulus of elastic objects from boundary displacement measurements","authors":"Yong Zhang, L. Hall, Dmitry Goldgof, S. Sarkar","doi":"10.1109/CEC.2002.1007062","DOIUrl":"https://doi.org/10.1109/CEC.2002.1007062","url":null,"abstract":"This paper presents a constrained genetic approach (CGA) for reconstructing the Young's modulus of elastic objects. Qualitative a priori information is incorporated using a rank based scheme to constrain the admissible solutions. Balance between the fitness function (adhesion to the measurement data) and the penalty function (fidelity to a priori knowledge) is achieved by a stochastic sort algorithm. The over-smoothing of Young's modulus discontinuity is avoided without the need of computing a deterministic weight coefficient. The experiment on synthetic data indicates that the proposed method not only reconstructed reliable Young's modulus from noisy data, but also expedited the convergence process significantly.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115446140","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004437
Sanjoy Das
Prokaryotes mainly reproduce by binary fission where the offspring are genetically identical to their parents. In order to take advantage of the benefits of sexual reproduction, these organisms have evolved ingenious ways to exchange genetic information. This article proposes an evolutionary algorithm called SPILL (Simulated Prokaryote Interchange of aLLeles) that is based on prokaryote genetic exchange patterns.
{"title":"Gene SPILL: an evolutionary algorithm based on bacterial gene exchange","authors":"Sanjoy Das","doi":"10.1109/CEC.2002.1004437","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004437","url":null,"abstract":"Prokaryotes mainly reproduce by binary fission where the offspring are genetically identical to their parents. In order to take advantage of the benefits of sexual reproduction, these organisms have evolved ingenious ways to exchange genetic information. This article proposes an evolutionary algorithm called SPILL (Simulated Prokaryote Interchange of aLLeles) that is based on prokaryote genetic exchange patterns.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125577673","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 : 2002-05-12DOI: 10.1109/CEC.2002.1004534
T. Bartz-Beielstein, J. Dienstuhl, Christian Feist, Marc Pompl
We demonstrate the applicability of evolutionary algorithms (EAs) to the optimization of circuit designs. We examine the design of a full-adder cell, and show the capability of design of experiments (DOE) methods to improve the parameter-settings of EAs.
{"title":"Circuit design using evolutionary algorithms","authors":"T. Bartz-Beielstein, J. Dienstuhl, Christian Feist, Marc Pompl","doi":"10.1109/CEC.2002.1004534","DOIUrl":"https://doi.org/10.1109/CEC.2002.1004534","url":null,"abstract":"We demonstrate the applicability of evolutionary algorithms (EAs) to the optimization of circuit designs. We examine the design of a full-adder cell, and show the capability of design of experiments (DOE) methods to improve the parameter-settings of EAs.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127076409","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 : 2002-05-12DOI: 10.1109/CEC.2002.1006201
S. J. Anastasoff
More than 10% of the human genome is comprised of a sequence known as an Alu repeat, with over a million copies of this distributed throughout our DNA. Detailed analysis of the distribution of this sequence shows it to be dispersed in an unusual way. In the work presented here, a genetic algorithm simulation was developed as the basis for modeling transposons (of which the Alu is one type). This simulation was used to explore the evolutionary conditions under which such a distribution could arise.
{"title":"The presence of old Alus in GC-rich regions of the human genome - a genetic algorithm perspective","authors":"S. J. Anastasoff","doi":"10.1109/CEC.2002.1006201","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006201","url":null,"abstract":"More than 10% of the human genome is comprised of a sequence known as an Alu repeat, with over a million copies of this distributed throughout our DNA. Detailed analysis of the distribution of this sequence shows it to be dispersed in an unusual way. In the work presented here, a genetic algorithm simulation was developed as the basis for modeling transposons (of which the Alu is one type). This simulation was used to explore the evolutionary conditions under which such a distribution could arise.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126195112","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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