Pub Date : 2010-07-18DOI: 10.1109/CEC.2010.5586026
S. Smit, A. Eiben
We present a case study demonstrating that using the REVAC parameter tuning method we can greatly improve the ‘world champion’ EA (the winner of the CEC-2005 competition) with little effort. For ‘normal’ EAs the margins for possible improvements are likely much bigger. Thus, the main message of this paper is that using REVAC great performance improvements are possible for many EAs at moderate costs. Our experiments also disclose the existence of ‘specialized generalists’, that is, EAs that are generally good on a set of test problems, but only w.r.t. one performance measure and not along another one. This shows that the notion of robust parameters is questionable and the issue requires further research. Finally, the results raise the question what the outcome of the CEC-2005 competition would have been, if all of EAs had been tuned by REVAC, but without further research it remains an open question whether we crowned the wrong king.
{"title":"Beating the ‘world champion’ evolutionary algorithm via REVAC tuning","authors":"S. Smit, A. Eiben","doi":"10.1109/CEC.2010.5586026","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586026","url":null,"abstract":"We present a case study demonstrating that using the REVAC parameter tuning method we can greatly improve the ‘world champion’ EA (the winner of the CEC-2005 competition) with little effort. For ‘normal’ EAs the margins for possible improvements are likely much bigger. Thus, the main message of this paper is that using REVAC great performance improvements are possible for many EAs at moderate costs. Our experiments also disclose the existence of ‘specialized generalists’, that is, EAs that are generally good on a set of test problems, but only w.r.t. one performance measure and not along another one. This shows that the notion of robust parameters is questionable and the issue requires further research. Finally, the results raise the question what the outcome of the CEC-2005 competition would have been, if all of EAs had been tuned by REVAC, but without further research it remains an open question whether we crowned the wrong king.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86338094","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586411
B. A. S. Hasan, J. Q. Gan, Qingfu Zhang
This paper presents a comparative study among three evolutionary and search based methods to solve the problem of channel selection for Brain-Computer Interface (BCI) systems. Multi-Objective Particle Swarm Optimization (MOPSO) method is compared to Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and single objective Sequential Floating Forward Search (SFFS) method. The methods are tested on the first data set for BCI-Competition IV. The results show the usefulness of the multi-objective evolutionary methods in achieving accuracy results similar to the extensive search method with fewer channels and less computational time.
{"title":"Multi-objective evolutionary methods for channel selection in Brain-Computer Interfaces: Some preliminary experimental results","authors":"B. A. S. Hasan, J. Q. Gan, Qingfu Zhang","doi":"10.1109/CEC.2010.5586411","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586411","url":null,"abstract":"This paper presents a comparative study among three evolutionary and search based methods to solve the problem of channel selection for Brain-Computer Interface (BCI) systems. Multi-Objective Particle Swarm Optimization (MOPSO) method is compared to Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and single objective Sequential Floating Forward Search (SFFS) method. The methods are tested on the first data set for BCI-Competition IV. The results show the usefulness of the multi-objective evolutionary methods in achieving accuracy results similar to the extensive search method with fewer channels and less computational time.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82664563","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 this paper, a novel self-constructing evolution algorithm (SCEA) for TSK-type fuzzy model (TFM) design is proposed. The proposed SCEA method is different from the traditional genetic algorithms (GA). A chromosome of the population in GA represents a full solution and only one population presents all solutions. Our method applies a population to evaluate a partial solution locally, and several populations to construct the full solution. Thus, a chromosome represents only partial solution. The proposed SCEA uses the self-constructing learning algorithm to construct the TFM automatically that is based on the input data to decide the input partition. And we also adopted the sequence search-based dynamic evolution (SSDE) method to perform parameter learning. Simulation results have shown that the proposed SCEA method obtains better performance than some existing models.
{"title":"A novel self-constructing evolution algorithm for TSK-type fuzzy model design","authors":"Sheng-Fuu Lin, Jyun-Wei Chang, Yi-Chang Cheng, Yung-Chi Hsu","doi":"10.1109/CEC.2010.5586205","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586205","url":null,"abstract":"In this paper, a novel self-constructing evolution algorithm (SCEA) for TSK-type fuzzy model (TFM) design is proposed. The proposed SCEA method is different from the traditional genetic algorithms (GA). A chromosome of the population in GA represents a full solution and only one population presents all solutions. Our method applies a population to evaluate a partial solution locally, and several populations to construct the full solution. Thus, a chromosome represents only partial solution. The proposed SCEA uses the self-constructing learning algorithm to construct the TFM automatically that is based on the input data to decide the input partition. And we also adopted the sequence search-based dynamic evolution (SSDE) method to perform parameter learning. Simulation results have shown that the proposed SCEA method obtains better performance than some existing models.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86768685","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 : 2010-07-18DOI: 10.1109/CEC.2010.5585954
S. Meshoul, M. Batouche
In an attempt to improve existing evolutionary metaheuristics quantum computing principles have been used. While some of them focus on the representation scheme adopted others deal with the behavior of the underlying algorithm. In this paper, we propose a search strategy that combines the ideas of use of a chaotic search with a selection operation within a quantum behaved Particle Swarm optimization algorithm. This search strategy is developed in order to achieve image alignment through maximization of an entropic measure: mutual information. The proposed framework is general as it handles any kind of transformation. Experimental results show the effectiveness of the algorithm to achieve good quality alignment for both mono modality and multimodality images. The proposed combination of the two features has lead to better solutions compared to those obtained by using each feature alone.
{"title":"A novel quantum behaved Particle Swarm optimization algorithm with chaotic search for image alignment","authors":"S. Meshoul, M. Batouche","doi":"10.1109/CEC.2010.5585954","DOIUrl":"https://doi.org/10.1109/CEC.2010.5585954","url":null,"abstract":"In an attempt to improve existing evolutionary metaheuristics quantum computing principles have been used. While some of them focus on the representation scheme adopted others deal with the behavior of the underlying algorithm. In this paper, we propose a search strategy that combines the ideas of use of a chaotic search with a selection operation within a quantum behaved Particle Swarm optimization algorithm. This search strategy is developed in order to achieve image alignment through maximization of an entropic measure: mutual information. The proposed framework is general as it handles any kind of transformation. Experimental results show the effectiveness of the algorithm to achieve good quality alignment for both mono modality and multimodality images. The proposed combination of the two features has lead to better solutions compared to those obtained by using each feature alone.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86773685","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586142
F. Vafaee, P. Nelson
Exploration and exploitation are the two cornerstones which characterize Evolutionary Algorithms (EAs) capabilities. Maintaining the reciprocal balance of the explorative and exploitative power is the key to the success of EA applications. Accordingly, in this work the canonical Genetic Algorithm is augmented by a new mutation scheme that is capable of exploring the unseen regions of the search space, and simultaneously exploiting the already-found promising elements. The proposed mutation operator specifies different mutation rates for different sites (loci) of the individuals. These site-specific rates are wisely derived based on the fitness and structure of the population individuals. In order to retain the balance of the required exploration and exploitation, the mutation rates are adapted during the evolution. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a set of benchmark problems and the outcome is compared with a series of well-known relevant algorithms. The results demonstrate that the newly suggested method significantly outperforms its rivals.
{"title":"An explorative and exploitative mutation scheme","authors":"F. Vafaee, P. Nelson","doi":"10.1109/CEC.2010.5586142","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586142","url":null,"abstract":"Exploration and exploitation are the two cornerstones which characterize Evolutionary Algorithms (EAs) capabilities. Maintaining the reciprocal balance of the explorative and exploitative power is the key to the success of EA applications. Accordingly, in this work the canonical Genetic Algorithm is augmented by a new mutation scheme that is capable of exploring the unseen regions of the search space, and simultaneously exploiting the already-found promising elements. The proposed mutation operator specifies different mutation rates for different sites (loci) of the individuals. These site-specific rates are wisely derived based on the fitness and structure of the population individuals. In order to retain the balance of the required exploration and exploitation, the mutation rates are adapted during the evolution. To demonstrate the efficacy of the proposed algorithm, the method is evaluated using a set of benchmark problems and the outcome is compared with a series of well-known relevant algorithms. The results demonstrate that the newly suggested method significantly outperforms its rivals.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91488618","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586421
N. Xiong, P. Funk
This paper proposes a new approach to discover knowledge about key features together with their degrees of importance in the context of case-based reasoning. A hierarchical memetic algorithm is designed for this purpose to search for the best feature subsets and similarity models at the same time. The objective of the memetic search is to optimize the possibility distributions derived for individual cases in the case library under a leave-one-out procedure. The information about the importance of selected features is revealed from the magnitudes of parameters of the learned similarity model. The effectiveness of the proposed approach has been shown by evaluation results on the benchmark data sets from the UCI repository and in comparisons with other machine learning techniques.
{"title":"Combined feature selection and similarity modelling in case-based reasoning using hierarchical memetic algorithm","authors":"N. Xiong, P. Funk","doi":"10.1109/CEC.2010.5586421","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586421","url":null,"abstract":"This paper proposes a new approach to discover knowledge about key features together with their degrees of importance in the context of case-based reasoning. A hierarchical memetic algorithm is designed for this purpose to search for the best feature subsets and similarity models at the same time. The objective of the memetic search is to optimize the possibility distributions derived for individual cases in the case library under a leave-one-out procedure. The information about the importance of selected features is revealed from the magnitudes of parameters of the learned similarity model. The effectiveness of the proposed approach has been shown by evaluation results on the benchmark data sets from the UCI repository and in comparisons with other machine learning techniques.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91550318","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586081
I. Pereira, A. Madureira
In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.
{"title":"Self-optimizing through CBR learning","authors":"I. Pereira, A. Madureira","doi":"10.1109/CEC.2010.5586081","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586081","url":null,"abstract":"In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80712166","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 : 2010-07-18DOI: 10.1109/CEC.2010.5585936
Amin Nikanjam, Hadi Sharifi, B. Helmi, A. Rahmani
Standard genetic algorithms are not very suited to problems with multivariate interactions among variables. This problem has been identified from the beginning of these algorithms and has been termed as the linkage learning problem. Numerous attempts have been carried out to solve this problem with various degree of success. In this paper, we employ an effective algorithm to cluster a dependency structure matrix (DSM) which can correctly identify the linkage groups. Once all the linkage groups are identified, a simple genetic algorithm using BB-wise crossover can easily solve hard optimization problems. Experimental results with a number of deceptive functions with various sizes presented to show the efficiency enhancement obtained by the proposed method. The results are also compared with Bayesian Optimization Algorithm, a well-known evolutionary optimizer, to demonstrate this improvement.
{"title":"Enhancing the efficiency of genetic algorithm by identifying linkage groups using DSM clustering","authors":"Amin Nikanjam, Hadi Sharifi, B. Helmi, A. Rahmani","doi":"10.1109/CEC.2010.5585936","DOIUrl":"https://doi.org/10.1109/CEC.2010.5585936","url":null,"abstract":"Standard genetic algorithms are not very suited to problems with multivariate interactions among variables. This problem has been identified from the beginning of these algorithms and has been termed as the linkage learning problem. Numerous attempts have been carried out to solve this problem with various degree of success. In this paper, we employ an effective algorithm to cluster a dependency structure matrix (DSM) which can correctly identify the linkage groups. Once all the linkage groups are identified, a simple genetic algorithm using BB-wise crossover can easily solve hard optimization problems. Experimental results with a number of deceptive functions with various sizes presented to show the efficiency enhancement obtained by the proposed method. The results are also compared with Bayesian Optimization Algorithm, a well-known evolutionary optimizer, to demonstrate this improvement.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83724642","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586276
Aniruddha Basak, S. Pal, Swagatam Das, A. Abraham, V. Snás̃el
Time modulated antenna arrays attracted the attention of researchers for the synthesis of low/ultra-low side lobes in recent past. In this article we propose an improved variant of a recently developed ecologically inspired metaheuristic, well-known as Invasive Weed Optimization (IWO), to solve the real parameter optimization problem related to the design of time-modulated linear antenna arrays with ultra low Side Lobe Level (SLL), Side Band Level (SBL) and Main Lobe Beam Width (BWFN). We improvise the classical IWO by introducing two parallel populations and a more explorative routine of changing the mutation step-size with iterations. Experimental results indicate that the proposed algorithm achieves better performance over the design problem as compared to the conventional Taylor Series based method and the only known metaheuristic approach based on the Differential Evolution (DE) algorithm.
{"title":"A modified Invasive Weed Optimization algorithm for time-modulated linear antenna array synthesis","authors":"Aniruddha Basak, S. Pal, Swagatam Das, A. Abraham, V. Snás̃el","doi":"10.1109/CEC.2010.5586276","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586276","url":null,"abstract":"Time modulated antenna arrays attracted the attention of researchers for the synthesis of low/ultra-low side lobes in recent past. In this article we propose an improved variant of a recently developed ecologically inspired metaheuristic, well-known as Invasive Weed Optimization (IWO), to solve the real parameter optimization problem related to the design of time-modulated linear antenna arrays with ultra low Side Lobe Level (SLL), Side Band Level (SBL) and Main Lobe Beam Width (BWFN). We improvise the classical IWO by introducing two parallel populations and a more explorative routine of changing the mutation step-size with iterations. Experimental results indicate that the proposed algorithm achieves better performance over the design problem as compared to the conventional Taylor Series based method and the only known metaheuristic approach based on the Differential Evolution (DE) algorithm.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83790136","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 : 2010-07-18DOI: 10.1109/CEC.2010.5586177
Jong-Min Park, Hyun-Chul Choi, Se-Young Oh
This paper proposes a method for reconstructing non-rigid 3D shapes from noisy 2D shapes. The proposed method estimates the 3D shape bases and projection matrices, exploiting low-rank constraints. Then the method finds the optimal coefficients for linear combinations of 3D shape bases to represent non-rigid 3D shapes using a genetic algorithm, and refines the 3D shape bases and the projection matrices using gradient descent techniques. The method reconstructed correct non-rigid 3D shapes in the presence of noise. The results can be used in many areas including animation, motion capture and non-rigid 3D object tracking.
{"title":"Non-rigid 3D face shape reconstruction using a genetic algorithm","authors":"Jong-Min Park, Hyun-Chul Choi, Se-Young Oh","doi":"10.1109/CEC.2010.5586177","DOIUrl":"https://doi.org/10.1109/CEC.2010.5586177","url":null,"abstract":"This paper proposes a method for reconstructing non-rigid 3D shapes from noisy 2D shapes. The proposed method estimates the 3D shape bases and projection matrices, exploiting low-rank constraints. Then the method finds the optimal coefficients for linear combinations of 3D shape bases to represent non-rigid 3D shapes using a genetic algorithm, and refines the 3D shape bases and the projection matrices using gradient descent techniques. The method reconstructed correct non-rigid 3D shapes in the presence of noise. The results can be used in many areas including animation, motion capture and non-rigid 3D object tracking.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83309679","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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