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}
Under a species-level abstraction of classical evolutionary programming, the standard tournament selection model is not appropriate. When viewed in this manner, it is more appropriate to consider two modes of life histories: background evolution and extinction. The utility of this approach as an optimization procedure is evaluated on a series of test functions relative to the performance of classical evolutionary programming and fast evolutionary programming. The results indicate that on some smooth, convex landscapes and over noisy, highly multimodal landscapes, extinction evolutionary programming can outperform classical and fast evolutionary programming. On other landscapes, however, extinction evolutionary programming performs considerably worse than classical and fast evolutionary programming. Potential reasons for this variability in performance are indicated.
{"title":"Evolutionary computation with extinction: experiments and analysis","authors":"G. Fogel, G. Greenwood, K. Chellapilla","doi":"10.1109/CEC.2000.870818","DOIUrl":"https://doi.org/10.1109/CEC.2000.870818","url":null,"abstract":"Under a species-level abstraction of classical evolutionary programming, the standard tournament selection model is not appropriate. When viewed in this manner, it is more appropriate to consider two modes of life histories: background evolution and extinction. The utility of this approach as an optimization procedure is evaluated on a series of test functions relative to the performance of classical evolutionary programming and fast evolutionary programming. The results indicate that on some smooth, convex landscapes and over noisy, highly multimodal landscapes, extinction evolutionary programming can outperform classical and fast evolutionary programming. On other landscapes, however, extinction evolutionary programming performs considerably worse than classical and fast evolutionary programming. Potential reasons for this variability in performance are indicated.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"350 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":"134371569","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}
In a quarter of a century of evolutionary computing, nature still seems to be teasing us with its complexity and flexibility whilst we struggle to apply our artificial creations, that perform so beautifully in blocks-world to the real world. We discuss some of the ways in which the biological world has seemed to defy the curse of dimensionality and present the results of an experiment to evolve neural network pattern detectors based on a pre-emptive 'phylogeny'. Strategies discussed are: congruent graduation of objective function and genome complexity; relaxation of objective function specificity; pre-evolved niche recombination; and fractal-like ontogenesis. A phyletic evolutionary architecture is proposed that combines these principles, together with three novel neural net transformations that preserve node-function integrity at different levels of complexity. Using a simple genetic algorithm, a number of 81-node fully recurrent neural nets were evolved to detect intermediate level features in 9/spl times/9 subimages. It is shown that by seeding the population with transformations of pre-evolved 3/spl times/3 detectors of constituent low-level features, evolution converged faster and to a more accurate and general solution than when they were evolved from a random population.
{"title":"Phyletic evolution of neural feature detectors","authors":"Peter R. W. Harvey, J. Boyce","doi":"10.1109/CEC.2000.870321","DOIUrl":"https://doi.org/10.1109/CEC.2000.870321","url":null,"abstract":"In a quarter of a century of evolutionary computing, nature still seems to be teasing us with its complexity and flexibility whilst we struggle to apply our artificial creations, that perform so beautifully in blocks-world to the real world. We discuss some of the ways in which the biological world has seemed to defy the curse of dimensionality and present the results of an experiment to evolve neural network pattern detectors based on a pre-emptive 'phylogeny'. Strategies discussed are: congruent graduation of objective function and genome complexity; relaxation of objective function specificity; pre-evolved niche recombination; and fractal-like ontogenesis. A phyletic evolutionary architecture is proposed that combines these principles, together with three novel neural net transformations that preserve node-function integrity at different levels of complexity. Using a simple genetic algorithm, a number of 81-node fully recurrent neural nets were evolved to detect intermediate level features in 9/spl times/9 subimages. It is shown that by seeding the population with transformations of pre-evolved 3/spl times/3 detectors of constituent low-level features, evolution converged faster and to a more accurate and general solution than when they were evolved from a random population.","PeriodicalId":218136,"journal":{"name":"Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)","volume":"24 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":"131600048","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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