A genetic algorithm based procedure for direct decision table adjustment is proposed to solve a multiobjective dynamic discrete-time optimal control problem. Multilevel coordinate control is introduced, the task of which is to coordinate and tune the control units according to the multiobjective overall criterion. The optimization of the cascade process according to the multiobjective overall criterion for minimal energy consumption and satisfying output constraints is carried out by means of a genetic algorithm. The proposed evolutionary optimization procedure of the multiobjective multilevel control is characterized by the simplicity of use and inherent adaptability.
{"title":"Multiobjective optimization of heat transfer plant using decision table controller and genetic algorithm","authors":"D. Grundler","doi":"10.1109/CEC.2000.870340","DOIUrl":"https://doi.org/10.1109/CEC.2000.870340","url":null,"abstract":"A genetic algorithm based procedure for direct decision table adjustment is proposed to solve a multiobjective dynamic discrete-time optimal control problem. Multilevel coordinate control is introduced, the task of which is to coordinate and tune the control units according to the multiobjective overall criterion. The optimization of the cascade process according to the multiobjective overall criterion for minimal energy consumption and satisfying output constraints is carried out by means of a genetic algorithm. The proposed evolutionary optimization procedure of the multiobjective multilevel control is characterized by the simplicity of use and inherent adaptability.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121896198","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}
Solving optimization problems with multiple (often conflicting) objectives is generally a quite difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade a multiplicity of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define and execute a quantitative MOEA performance comparison methodology. Almost all comparisons cited in the current literature visually compare algorithmic results, resulting in only relative conclusions. Our methodology gives a basis for absolute conclusions regarding MOEA performance. Selected results from its execution with four MOEAs are presented and described.
{"title":"On measuring multiobjective evolutionary algorithm performance","authors":"D. V. Veldhuizen, G. Lamont","doi":"10.1109/CEC.2000.870296","DOIUrl":"https://doi.org/10.1109/CEC.2000.870296","url":null,"abstract":"Solving optimization problems with multiple (often conflicting) objectives is generally a quite difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade a multiplicity of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define and execute a quantitative MOEA performance comparison methodology. Almost all comparisons cited in the current literature visually compare algorithmic results, resulting in only relative conclusions. Our methodology gives a basis for absolute conclusions regarding MOEA performance. Selected results from its execution with four MOEAs are presented and described.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121681595","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}
A new genetic algorithm based method for solving nonlinear multicriterion optimization problems is described. The method does not use a fitness value as a measure, as a genetic algorithm uses to create the population of chromosomes for the next generation. The proposed method uses tournament selection which does not require evaluation of fitness values in order to create a new population of chromosomes for the next generation. The tournament is arranged such that objective functions are evaluated only for feasible solutions. After a detailed description of the method two examples are presented and the results are compared with those obtained using other methods. This comparison shows the effectiveness of the proposed method.
{"title":"A new constraint tournament selection method for multicriteria optimization using genetic algorithm","authors":"O. Andrzej, K. Stanislaw","doi":"10.1109/CEC.2000.870338","DOIUrl":"https://doi.org/10.1109/CEC.2000.870338","url":null,"abstract":"A new genetic algorithm based method for solving nonlinear multicriterion optimization problems is described. The method does not use a fitness value as a measure, as a genetic algorithm uses to create the population of chromosomes for the next generation. The proposed method uses tournament selection which does not require evaluation of fitness values in order to create a new population of chromosomes for the next generation. The tournament is arranged such that objective functions are evaluated only for feasible solutions. After a detailed description of the method two examples are presented and the results are compared with those obtained using other methods. This comparison shows the effectiveness of the proposed method.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131280346","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}
Though it has been claimed that elitism could improve evolutionary multi-objective search significantly, a thorough and extensive evaluation of its effects is still missing. Guidelines on how elitism could successfully be incorporated have not yet been developed. This paper presents a unified model of multi-objective evolutionary algorithms, in which arbitrary variation and selection operators can be combined as building blocks, including archiving and re-insertion strategies. The presented model enables most specific multi-objective (evolutionary) algorithm to be formulated as an instance of it, which will be demonstrated by simple examples. We further show how elitism can be quantified by the model's parameters and how this allows an easy evaluation of the effect of elitism on different algorithms.
{"title":"A unified model for multi-objective evolutionary algorithms with elitism","authors":"M. Laumanns, E. Zitzler, Lothar Thiele","doi":"10.1109/CEC.2000.870274","DOIUrl":"https://doi.org/10.1109/CEC.2000.870274","url":null,"abstract":"Though it has been claimed that elitism could improve evolutionary multi-objective search significantly, a thorough and extensive evaluation of its effects is still missing. Guidelines on how elitism could successfully be incorporated have not yet been developed. This paper presents a unified model of multi-objective evolutionary algorithms, in which arbitrary variation and selection operators can be combined as building blocks, including archiving and re-insertion strategies. The presented model enables most specific multi-objective (evolutionary) algorithm to be formulated as an instance of it, which will be demonstrated by simple examples. We further show how elitism can be quantified by the model's parameters and how this allows an easy evaluation of the effect of elitism on different algorithms.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"21 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131495575","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}
Frequency sampling is one of the more usual methods in FIR digital filter design. In the frequency-sampling method, the values of transition-band samples, which are usually obtained by searching a table, must be determined in order to make the attenuation within the stop-band maximal. However, the value obtained by searching the table cannot be ensured to be optimal. Evolutionary programming (EP), a multi-agent stochastic optimization technique, can lead to globally optimal solutions for complex problems. In this paper, a new application of EP to the frequency-sampling method is introduced. Three examples of FIR filter design are presented, and the steps of EP realization and experimental results are given. The experimental results have shown that the values of transition-band samples obtained by EP can be ensured to be optimal and the performance of the filter is improved.
{"title":"FIR filter design: frequency-sampling method based on evolutionary programming","authors":"X. Chen, S. L. Yu","doi":"10.1109/CEC.2000.870348","DOIUrl":"https://doi.org/10.1109/CEC.2000.870348","url":null,"abstract":"Frequency sampling is one of the more usual methods in FIR digital filter design. In the frequency-sampling method, the values of transition-band samples, which are usually obtained by searching a table, must be determined in order to make the attenuation within the stop-band maximal. However, the value obtained by searching the table cannot be ensured to be optimal. Evolutionary programming (EP), a multi-agent stochastic optimization technique, can lead to globally optimal solutions for complex problems. In this paper, a new application of EP to the frequency-sampling method is introduced. Three examples of FIR filter design are presented, and the steps of EP realization and experimental results are given. The experimental results have shown that the values of transition-band samples obtained by EP can be ensured to be optimal and the performance of the filter is improved.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128210072","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}
Genetic programming (GP) was used in an experiment to investigate the possibility of learning rules that trigger adaptive mesh refinement. GP detected mesh cells that required refinement by evolving a formula involving cell quantities such as material densities. Various cell variable combinations were investigated in order to identify the optimal ones for indicating mesh refinement. The problem studied was the high speed impact of a spherical ball on a metal plate.
{"title":"Evolution of mesh refinement rules for impact dynamics","authors":"D. Howard, S. C. Roberts","doi":"10.1109/CEC.2000.870801","DOIUrl":"https://doi.org/10.1109/CEC.2000.870801","url":null,"abstract":"Genetic programming (GP) was used in an experiment to investigate the possibility of learning rules that trigger adaptive mesh refinement. GP detected mesh cells that required refinement by evolving a formula involving cell quantities such as material densities. Various cell variable combinations were investigated in order to identify the optimal ones for indicating mesh refinement. The problem studied was the high speed impact of a spherical ball on a metal plate.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881104","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}
Model induction plays an important role in many fields of science and engineering to analyze data. Specifically, the performance of time series prediction whose objectives are to find out the dynamics of the underlying process in given data is greatly affected by the model. Bayesian evolutionary algorithms have been proposed as a method for automatic model induction from data. We apply Bayesian evolutionary algorithms (BEAs) to evolving neural tree models of time series data. The performances of various BEAs are compared on two time series prediction problems by varying the population size and the type of variation operations. Our experimental results support that population based BEAs with unlimited crossover find good models more efficiently than single individual BEAs, parallelized individual based BEAs, and population based BEAs with limited crossover.
{"title":"Bayesian evolutionary algorithms for evolving neural tree models of time series data","authors":"Dong-Yeon Cho, Byoung-Tak Zhang","doi":"10.1109/CEC.2000.870825","DOIUrl":"https://doi.org/10.1109/CEC.2000.870825","url":null,"abstract":"Model induction plays an important role in many fields of science and engineering to analyze data. Specifically, the performance of time series prediction whose objectives are to find out the dynamics of the underlying process in given data is greatly affected by the model. Bayesian evolutionary algorithms have been proposed as a method for automatic model induction from data. We apply Bayesian evolutionary algorithms (BEAs) to evolving neural tree models of time series data. The performances of various BEAs are compared on two time series prediction problems by varying the population size and the type of variation operations. Our experimental results support that population based BEAs with unlimited crossover find good models more efficiently than single individual BEAs, parallelized individual based BEAs, and population based BEAs with limited crossover.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132180964","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}
S. B. Hussein, H. Jamaluddin, M. Mailah, A. Zalzala
In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. The EC part of the algorithm composes of a hybrid genetic algorithm (GA) and an evolutionary program (EP). The development of the controller is divided into two stages. In the first stage, which is performed off-line, the proposed EC algorithm is employed to evolve a pool of ANN structures until they converge to an optimum structure. The population is divided into different groups according to their fitness. The elitist group will not undergo any operation, while the second group, i.e. stronger group, undergoes the EP operation. Hence, the behavioral link between the parent and their offspring can be maintained. The weaker group undergoes a GA operation since their behaviors need to be changed more effectively in order to produce better offspring. In the second stage, the evolved ANN obtained from the first stage, which represent the optimum ANN structural design, is used to design the on-line intelligent parameter estimator to estimate the inertia matrix of the robot arm for the AFC controller. In this on-line stage, the ANN parameters, i.e. the weights and biases, are further trained using live data and back-propagation until a satisfactory result is obtained. The effectiveness of the proposed scheme is demonstrated through a simulation study performed on a two link planar manipulator operating in a horizontal plane. An external load is introduced to the manipulator to study the effectiveness of the proposed scheme.
{"title":"A hybrid intelligent active force controller for robot arms using evolutionary neural networks","authors":"S. B. Hussein, H. Jamaluddin, M. Mailah, A. Zalzala","doi":"10.1109/CEC.2000.870284","DOIUrl":"https://doi.org/10.1109/CEC.2000.870284","url":null,"abstract":"In this paper, we propose a hybrid intelligent parameter estimator for the active force control (AFC) scheme which utilizes evolutionary computation (EC) and artificial neural networks (ANN) to control a rigid robot arm. The EC part of the algorithm composes of a hybrid genetic algorithm (GA) and an evolutionary program (EP). The development of the controller is divided into two stages. In the first stage, which is performed off-line, the proposed EC algorithm is employed to evolve a pool of ANN structures until they converge to an optimum structure. The population is divided into different groups according to their fitness. The elitist group will not undergo any operation, while the second group, i.e. stronger group, undergoes the EP operation. Hence, the behavioral link between the parent and their offspring can be maintained. The weaker group undergoes a GA operation since their behaviors need to be changed more effectively in order to produce better offspring. In the second stage, the evolved ANN obtained from the first stage, which represent the optimum ANN structural design, is used to design the on-line intelligent parameter estimator to estimate the inertia matrix of the robot arm for the AFC controller. In this on-line stage, the ANN parameters, i.e. the weights and biases, are further trained using live data and back-propagation until a satisfactory result is obtained. The effectiveness of the proposed scheme is demonstrated through a simulation study performed on a two link planar manipulator operating in a horizontal plane. An external load is introduced to the manipulator to study the effectiveness of the proposed scheme.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132417486","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}
Hybrid differential evolution is applied to estimate the kinetic model parameters of batch fermentation for ethanol and glycerol production using Saccharomyces diastaticus LORRE 316. In this study, we consider the worst observed error for all experiments as an objective function so that the parameter estimation problem becomes a min-max estimation problem. Several methods have been employed to solve the min-max estimation problem for comparison. The proposed method can use a small population size to obtain a more satisfactory solution as compared from these computations. In order to validate the kinetic model, we have carried out the fedbatch experiments with an optimal feed rate. The experimental data can fit the computed results satisfactorily.
{"title":"Parameter estimation of a bioreaction model by hybrid differential evolution","authors":"Feng-Sheng Wang, Horng-Jhy Jang","doi":"10.1109/CEC.2000.870325","DOIUrl":"https://doi.org/10.1109/CEC.2000.870325","url":null,"abstract":"Hybrid differential evolution is applied to estimate the kinetic model parameters of batch fermentation for ethanol and glycerol production using Saccharomyces diastaticus LORRE 316. In this study, we consider the worst observed error for all experiments as an objective function so that the parameter estimation problem becomes a min-max estimation problem. Several methods have been employed to solve the min-max estimation problem for comparison. The proposed method can use a small population size to obtain a more satisfactory solution as compared from these computations. In order to validate the kinetic model, we have carried out the fedbatch experiments with an optimal feed rate. The experimental data can fit the computed results satisfactorily.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133046366","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}
Proposes a divided-range multi-objective genetic algorithm (DRMOGA), which is a model for the parallel processing of genetic algorithms (GAs) for multi-objective problems. In the DRMOGA, the population of GAs is sorted with respect to the values of the objective function and divided into sub-populations. In each sub-population, a simple GA for multi-objective problems is performed. After some generations, all the individuals are gathered and they are sorted again. In this model, the Pareto-optimal solutions which are close to each other are collected into one sub-population. Therefore, this algorithm increases the calculation efficiency and a neighborhood search can be performed. Through numerical examples, the following facts become clear: (i) the DRMOGA is a very suitable GA model for parallel processing, and (ii) in some cases it can derive better solutions compared to both the single-population model and the distributed model.
{"title":"The new model of parallel genetic algorithm in multi-objective optimization problems - divided range multi-objective genetic algorithm","authors":"T. Hiroyasu, M. Miki, S. Watanabe","doi":"10.1109/CEC.2000.870314","DOIUrl":"https://doi.org/10.1109/CEC.2000.870314","url":null,"abstract":"Proposes a divided-range multi-objective genetic algorithm (DRMOGA), which is a model for the parallel processing of genetic algorithms (GAs) for multi-objective problems. In the DRMOGA, the population of GAs is sorted with respect to the values of the objective function and divided into sub-populations. In each sub-population, a simple GA for multi-objective problems is performed. After some generations, all the individuals are gathered and they are sorted again. In this model, the Pareto-optimal solutions which are close to each other are collected into one sub-population. Therefore, this algorithm increases the calculation efficiency and a neighborhood search can be performed. Through numerical examples, the following facts become clear: (i) the DRMOGA is a very suitable GA model for parallel processing, and (ii) in some cases it can derive better solutions compared to both the single-population model and the distributed model.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130151818","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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