M. Milano, P. Koumoutsakos, X. Giannakopoulos, J. Schmidhuber
Rechenberg and Schwefel (Rechenberg, 1994) came up with the idea of evolution strategies for flow optimization. Since then advances in computer architectures and numerical algorithms have greatly decreased computational costs of realistic flow simulations, and today computational fluid dynamics (CFD) is complementing flow experiments as a key guiding tool for aerodynamic design. Of particular interest are designs with active devices controlling the inherently unsteady flow fields, promising potentially drastic performance leaps. We demonstrate that CFD-based design of active control strategies can benefit from evolutionary computation. We optimize the flow past an actively controlled circular cylinder, a fundamental prototypical configuration. The flow is controlled using surface-mounted vortex generators; evolutionary algorithms are used to optimize actuator placement and operating parameters. We achieve drag reduction of up to 60 percent, outperforming the best methods previously reported in the fluid dynamics literature on this benchmark problem.
{"title":"Evolving strategies for active flow control","authors":"M. Milano, P. Koumoutsakos, X. Giannakopoulos, J. Schmidhuber","doi":"10.1109/CEC.2000.870297","DOIUrl":"https://doi.org/10.1109/CEC.2000.870297","url":null,"abstract":"Rechenberg and Schwefel (Rechenberg, 1994) came up with the idea of evolution strategies for flow optimization. Since then advances in computer architectures and numerical algorithms have greatly decreased computational costs of realistic flow simulations, and today computational fluid dynamics (CFD) is complementing flow experiments as a key guiding tool for aerodynamic design. Of particular interest are designs with active devices controlling the inherently unsteady flow fields, promising potentially drastic performance leaps. We demonstrate that CFD-based design of active control strategies can benefit from evolutionary computation. We optimize the flow past an actively controlled circular cylinder, a fundamental prototypical configuration. The flow is controlled using surface-mounted vortex generators; evolutionary algorithms are used to optimize actuator placement and operating parameters. We achieve drag reduction of up to 60 percent, outperforming the best methods previously reported in the fluid dynamics literature on this benchmark problem.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"20 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":"123776144","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}
We investigate stochastic local search by Markov chain analysis in a high and a low dimensional discrete space. In the n-dimensional space B/sup n/ a function called Jump is considered. The analysis shows that an algorithm using a large neighborhood and never accepting worse points performs much better than any local search algorithm accepting worse points with a certain probability. We also investigate functions in the space B/sup n/ with many local optima. We compare stochastic local search using large neighborhoods with a local search using optimal temperature schedules which depend on the state of the Markov process.
{"title":"Size of neighborhood more important than temperature for stochastic local search","authors":"H. Mühlenbein, Jörg Zimmermann","doi":"10.1109/CEC.2000.870758","DOIUrl":"https://doi.org/10.1109/CEC.2000.870758","url":null,"abstract":"We investigate stochastic local search by Markov chain analysis in a high and a low dimensional discrete space. In the n-dimensional space B/sup n/ a function called Jump is considered. The analysis shows that an algorithm using a large neighborhood and never accepting worse points performs much better than any local search algorithm accepting worse points with a certain probability. We also investigate functions in the space B/sup n/ with many local optima. We compare stochastic local search using large neighborhoods with a local search using optimal temperature schedules which depend on the state of the Markov process.","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":"123811044","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}
H. Martínez, A. Gómez-Skarmeta, F. Jiménez, Miguel Zamora
In this paper, we present evolutionary techniques to solve the problem of the conflicts between different behaviours in the context of an autonomous mobile robot. We also describe the working environment, based on a custom programming language (named BG after its inventors, Barber/spl acute/a and Go/spl acute/mez, 1996) and an agent architecture, where we test a series of behaviours that were developed using fuzzy logic. Finally, some results related to a simple navigational task in an unknown environment are presented.
{"title":"Evolutionary computation techniques for behaviour fusion in autonomous mobile robots","authors":"H. Martínez, A. Gómez-Skarmeta, F. Jiménez, Miguel Zamora","doi":"10.1109/CEC.2000.870380","DOIUrl":"https://doi.org/10.1109/CEC.2000.870380","url":null,"abstract":"In this paper, we present evolutionary techniques to solve the problem of the conflicts between different behaviours in the context of an autonomous mobile robot. We also describe the working environment, based on a custom programming language (named BG after its inventors, Barber/spl acute/a and Go/spl acute/mez, 1996) and an agent architecture, where we test a series of behaviours that were developed using fuzzy logic. Finally, some results related to a simple navigational task in an unknown environment are presented.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"243 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":"122721856","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}
Given a collection of biologically related protein or DNA sequences, the basic multiple sequence alignment problem is to determine the most biologically plausible alignment of these sequences. Under the assumption that the collection of sequences arose from some common ancestor, an alignment can be used to infer the evolutionary history among the sequences, i.e., the most likely pattern of insertions, deletions and mutations that transformed one sequence into another. The general multiple sequence alignment problem is known to be NP-hard, and hence the problem of finding the best possible multiple sequence alignment is intractable. However, this does not preclude the possibility of developing algorithms that produce near optimal multiple sequence alignments in polynomial time. We examine techniques to combine efficient algorithms for near optimal global and local multiple sequence alignment with evolutionary computation techniques to search for better near optimal sequence alignments. We describe our evolutionary computation approach to multiple sequence alignment and present preliminary simulation results on a set of 17 clusters of orthologous groups of proteins (COGs). We compare the fitness of the alignments given by the proposed techniques with the fitness of CLUSTAL W alignments given in the COG database.
{"title":"Evolutionary computation techniques for multiple sequence alignment","authors":"L. Cai, D. Juedes, Evgueni Liakhovitch","doi":"10.1109/CEC.2000.870716","DOIUrl":"https://doi.org/10.1109/CEC.2000.870716","url":null,"abstract":"Given a collection of biologically related protein or DNA sequences, the basic multiple sequence alignment problem is to determine the most biologically plausible alignment of these sequences. Under the assumption that the collection of sequences arose from some common ancestor, an alignment can be used to infer the evolutionary history among the sequences, i.e., the most likely pattern of insertions, deletions and mutations that transformed one sequence into another. The general multiple sequence alignment problem is known to be NP-hard, and hence the problem of finding the best possible multiple sequence alignment is intractable. However, this does not preclude the possibility of developing algorithms that produce near optimal multiple sequence alignments in polynomial time. We examine techniques to combine efficient algorithms for near optimal global and local multiple sequence alignment with evolutionary computation techniques to search for better near optimal sequence alignments. We describe our evolutionary computation approach to multiple sequence alignment and present preliminary simulation results on a set of 17 clusters of orthologous groups of proteins (COGs). We compare the fitness of the alignments given by the proposed techniques with the fitness of CLUSTAL W alignments given in the COG database.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"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":"121791481","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 novel approach to design robust nonlinear control laws for dynamic systems that are not in canonical form or unknown is introduced. A competitive coevolutionary algorithm is used to design a sliding mode controller based on an approximate model of both the system and uncertainties. The power of evolutionary computation leads to a systematic convergence on accurate nominal model based controllers and the use of competitive coevolution offers a new method to handle model uncertainties with a sliding control structure.
{"title":"Robust nonlinear control design using competitive coevolution","authors":"J. Claverie, K. D. Jong, A. Sheta","doi":"10.1109/CEC.2000.870324","DOIUrl":"https://doi.org/10.1109/CEC.2000.870324","url":null,"abstract":"A novel approach to design robust nonlinear control laws for dynamic systems that are not in canonical form or unknown is introduced. A competitive coevolutionary algorithm is used to design a sliding mode controller based on an approximate model of both the system and uncertainties. The power of evolutionary computation leads to a systematic convergence on accurate nominal model based controllers and the use of competitive coevolution offers a new method to handle model uncertainties with a sliding control structure.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"8 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":"121879589","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}
Presents a classification algorithm based on genetic algorithms (GAs) that discovers comprehensible IF-THEN rules, in the spirit of data mining. The proposed GA has a flexible chromosome encoding, where each chromosome corresponds to a classification rule. Although the number of genes (the genotype) is fixed, the number of rule conditions (the phenotype) is variable. The GA also has specific mutation operators for this chromosome encoding. The algorithm was evaluated on two public-domain real-world data sets (in the medical domains of dermatology and breast cancer).
{"title":"Discovering comprehensible classification rules with a genetic algorithm","authors":"M. Fidelis, H. S. Lopes, A. Freitas","doi":"10.1109/CEC.2000.870381","DOIUrl":"https://doi.org/10.1109/CEC.2000.870381","url":null,"abstract":"Presents a classification algorithm based on genetic algorithms (GAs) that discovers comprehensible IF-THEN rules, in the spirit of data mining. The proposed GA has a flexible chromosome encoding, where each chromosome corresponds to a classification rule. Although the number of genes (the genotype) is fixed, the number of rule conditions (the phenotype) is variable. The GA also has specific mutation operators for this chromosome encoding. The algorithm was evaluated on two public-domain real-world data sets (in the medical domains of dermatology and breast cancer).","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"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":"128024149","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}
We propose a new computing model, computational living systems. We use the chemical system in our model as it is the basic system of living things. The real biochemical system is so complicated that it is hard to deal with. We use an abstract chemical system by using the multiset rewriting system, Abstract Rewriting System on Multisets (ARMS) that is also a multiset transform system. Considering that a membrane is an important structure for a living thing to separate 'self' from its environment as well as many organelles inside are composed of membranes, we introduce the membrane structure in ARMS. We further develop an artificial cell system (ACS) and investigate the behavior of ACS under various environments by introducing a genetic method and using genetic programming.
{"title":"Computational living systems based on an abstract chemical system","authors":"Y. Suzuki, H. Tanaka","doi":"10.1109/CEC.2000.870812","DOIUrl":"https://doi.org/10.1109/CEC.2000.870812","url":null,"abstract":"We propose a new computing model, computational living systems. We use the chemical system in our model as it is the basic system of living things. The real biochemical system is so complicated that it is hard to deal with. We use an abstract chemical system by using the multiset rewriting system, Abstract Rewriting System on Multisets (ARMS) that is also a multiset transform system. Considering that a membrane is an important structure for a living thing to separate 'self' from its environment as well as many organelles inside are composed of membranes, we introduce the membrane structure in ARMS. We further develop an artificial cell system (ACS) and investigate the behavior of ACS under various environments by introducing a genetic method and using genetic programming.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"40 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":"134038871","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 methodology named Evolutionary Artificial Potential Field (EAPF) is proposed for real-time robot path planning. The artificial potential field method is combined with genetic algorithms, to derive optimal potential field functions. The proposed EAPF approach is capable of navigating robot(s) situated among moving obstacles. Potential field functions for obstacles and goal points are also defined. The potential field functions for obstacles contain tunable parameters. The multi-objective evolutionary algorithm (MOEA) is utilized to identify the optimal potential field functions. Fitness functions such as goal-factor, obstacle-factor, smoothness-factor and minimum-pathlength-factor are developed for the MOEA selection criteria. An algorithm named escape-force is introduced to avoid the local minima associated with EAPF. Moving obstacles and moving goal positions were considered to test the robust performance of the proposed methodology. Simulation results show that the proposed methodology is efficient and robust for robot path planning with non-stationary goals and obstacles.
{"title":"Evolutionary artificial potential fields and their application in real time robot path planning","authors":"P. Vadakkepat, K. Tan, Ming-Liang Wang","doi":"10.1109/CEC.2000.870304","DOIUrl":"https://doi.org/10.1109/CEC.2000.870304","url":null,"abstract":"A new methodology named Evolutionary Artificial Potential Field (EAPF) is proposed for real-time robot path planning. The artificial potential field method is combined with genetic algorithms, to derive optimal potential field functions. The proposed EAPF approach is capable of navigating robot(s) situated among moving obstacles. Potential field functions for obstacles and goal points are also defined. The potential field functions for obstacles contain tunable parameters. The multi-objective evolutionary algorithm (MOEA) is utilized to identify the optimal potential field functions. Fitness functions such as goal-factor, obstacle-factor, smoothness-factor and minimum-pathlength-factor are developed for the MOEA selection criteria. An algorithm named escape-force is introduced to avoid the local minima associated with EAPF. Moving obstacles and moving goal positions were considered to test the robust performance of the proposed methodology. Simulation results show that the proposed methodology is efficient and robust for robot path planning with non-stationary goals and obstacles.","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":"134560061","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}
M. Martín-Bautista, M. Vila, D. Sánchez, H. Larsen
An improvement in the effectiveness of information retrieval by using genetic algorithms (GAs) and fuzzy logic is demonstrated. A new classification of information retrieval models within the framework of GAs is given. Such a classification is based on the target of the fitness function selected. When the aim of the optimization is document classification, we deal with document-oriented models. On the other hand, term-oriented models attempt to find those terms that are more discriminatory and adequate for user preferences to build a profile. A new weighting scheme based on fuzzy logic is presented for the first class of models. A comparison with other classical weighting schemes and a study of the best aggregation operators of the gene's local fitness to the overall fitness per chromosome are also presented. The deeper study of this new scheme in the term-oriented models is the main objective for future work.
{"title":"Fuzzy genes: improving the effectiveness of information retrieval","authors":"M. Martín-Bautista, M. Vila, D. Sánchez, H. Larsen","doi":"10.1109/CEC.2000.870334","DOIUrl":"https://doi.org/10.1109/CEC.2000.870334","url":null,"abstract":"An improvement in the effectiveness of information retrieval by using genetic algorithms (GAs) and fuzzy logic is demonstrated. A new classification of information retrieval models within the framework of GAs is given. Such a classification is based on the target of the fitness function selected. When the aim of the optimization is document classification, we deal with document-oriented models. On the other hand, term-oriented models attempt to find those terms that are more discriminatory and adequate for user preferences to build a profile. A new weighting scheme based on fuzzy logic is presented for the first class of models. A comparison with other classical weighting schemes and a study of the best aggregation operators of the gene's local fitness to the overall fitness per chromosome are also presented. The deeper study of this new scheme in the term-oriented models is the main objective for future work.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"22 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":"114861641","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}
Applications of genetic algorithms (GA) for optimisation problems are widely known as well as their advantages and disadvantages compared with classical numerical methods. In practical tests, GA appears a robust method with a broad range of applications. The determination of GA parameters could be complicated. Therefore for some real-life applications, several empirical observations of an experienced expert are needed to define these parameters. This fact degrades the applicability of a GA for most of the real-world problems and users. Therefore, this article discusses some possibilities with setting GA parameters. The setting method of GA parameters is based on the fuzzy control of values of GA parameters. The feedback for the fuzzy control of GA parameters is realized by virtue of the behavior of some GA characteristics. The goal of this article is to present the conception of the solution and some new ideas.
{"title":"GA with fuzzy inference system","authors":"R. Matousek, P. Osmera, J. Roupec","doi":"10.1109/CEC.2000.870359","DOIUrl":"https://doi.org/10.1109/CEC.2000.870359","url":null,"abstract":"Applications of genetic algorithms (GA) for optimisation problems are widely known as well as their advantages and disadvantages compared with classical numerical methods. In practical tests, GA appears a robust method with a broad range of applications. The determination of GA parameters could be complicated. Therefore for some real-life applications, several empirical observations of an experienced expert are needed to define these parameters. This fact degrades the applicability of a GA for most of the real-world problems and users. Therefore, this article discusses some possibilities with setting GA parameters. The setting method of GA parameters is based on the fuzzy control of values of GA parameters. The feedback for the fuzzy control of GA parameters is realized by virtue of the behavior of some GA characteristics. The goal of this article is to present the conception of the solution and some new ideas.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"9 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":"115384112","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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