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2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)最新文献

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Genetic Algorithm-based ecosystem for heather management 基于遗传算法的石南植物管理生态系统
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-09-23 DOI: 10.1109/CEC.2008.4631242
N. Jin
{"title":"Genetic Algorithm-based ecosystem for heather management","authors":"N. Jin","doi":"10.1109/CEC.2008.4631242","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631242","url":null,"abstract":"This paper applies genetic algorithms (GA) to simulate a heather moorland ecosystem. We investigate, in this ecosystem how to manage heather for the benefits of survival and reproduction of grouse. A GA candidate solution is a grid, representing spatial relationship of three types of heather. From solutions provided by GA, we have found that the diversity of neighborhood and its distribution are essential. The evenly diversified heather distributions emerge as the best fit solutions for grousepsilas needs. We compared this finding with data collected from the field work.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122780771","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}
引用次数: 0
Supporting component selection with a suite of classifiers 使用一组分类器支持组件选择
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-09-23 DOI: 10.1109/CEC.2008.4631334
Valerie Maxville, C. Lam, J. Armarego
{"title":"Supporting component selection with a suite of classifiers","authors":"Valerie Maxville, C. Lam, J. Armarego","doi":"10.1109/CEC.2008.4631334","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631334","url":null,"abstract":"Software selection involves the assimilation of information and results for each candidate to enable a comparison for decisions to be made. The processes and tools developed assist with software selection to enhance quality, documentation and repeatability. The CdCE process aims to retain and document the information used in selection to assist decisions and to document them for reference as the system evolves. This paper describes the CdCE process and our approach to assist the shortlisting of candidates through a suite of classifiers. The application of the suite is illustrated using a selection and evaluation case study. Applying this approach helps retain the multidimensional nature of the selection process and enhances user awareness in the decision making process.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123065985","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}
引用次数: 2
Evolutionary route to computation in self-assembled nanoarrays 自组装纳米阵列计算的进化路径
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-09-23 DOI: 10.1109/CEC.2008.4631216
S. Benjamin
{"title":"Evolutionary route to computation in self-assembled nanoarrays","authors":"S. Benjamin","doi":"10.1109/CEC.2008.4631216","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631216","url":null,"abstract":"Ordered nanoarrays, i.e. regular patterns of quantum structures at the nanometre scale, can now be synthesized in a range of systems. In this paper I study a form of array computation where the internal dynamics are driven by intrinsic cell-cell interactions and global optical pulses addressing entire structure indiscriminately. The array would need to be dasiawiredpsila to conventional technologies only at its boundary. Any self-assembled array would have a unique set of defects, therefore I employ an ab initio evolutionary process to subsume such flaws without any need to determine their location or nature. The approach succeeds for various forms of physical interaction within the array.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121080280","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}
引用次数: 0
Adequate determination of a band of wavelet threshold for noise cancellation using particle swarm optimization 利用粒子群优化确定小波阈值以消除噪声
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630944
Tsung-Ying Sun, Chan-Cheng Liu, Tsung-Ying Tsai, Sheng-Ta Hsieh
{"title":"Adequate determination of a band of wavelet threshold for noise cancellation using particle swarm optimization","authors":"Tsung-Ying Sun, Chan-Cheng Liu, Tsung-Ying Tsai, Sheng-Ta Hsieh","doi":"10.1109/CEC.2008.4630944","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630944","url":null,"abstract":"Noise reduction problem is addressed by this study. Recently, wavelet thresholding has become popular and has gotten much attention among a number of de-noisy approaches. The most of threshold determination are developed from universal method proposed by Donoho. But, some shortcomings of the determination are caused from several incorrectly estimated factors and the lack of adaptability for whole frequency. By the reason, this paper replaces a universal threshold by multi-thresholds for matching the coefficients of each wavelet segment, and then the band of threshold will be fined by particle swarm optimization (PSO). Because original signals and noise are mutually independent, an objective function of PSO is created to evaluate the second order correlation and high order correlation. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with high or low noise degree are designed. Moreover, the performance of proposed algorithm will have compared with that of other existing algorithms.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114989960","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}
引用次数: 10
Particle swarm optimization applied to the chess game 粒子群算法在棋类游戏中的应用
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631299
João A. Duro, José Valente de Oliveira
{"title":"Particle swarm optimization applied to the chess game","authors":"João A. Duro, José Valente de Oliveira","doi":"10.1109/CEC.2008.4631299","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631299","url":null,"abstract":"To the best of the authorspsila knowledge this paper investigates for the first time the applicability of particle swarm optimization (PSO) to a chess player agent endowing it with learning abilities, i.e. allowing the agent to improve its performance based on its experience.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"64 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115394500","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}
引用次数: 17
Online adaptive controller for simulated car racing 模拟赛车的在线自适应控制器
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631096
Chin Hiong Tan, J. Ang, K. Tan, A. Tay
{"title":"Online adaptive controller for simulated car racing","authors":"Chin Hiong Tan, J. Ang, K. Tan, A. Tay","doi":"10.1109/CEC.2008.4631096","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631096","url":null,"abstract":"An adaptive game AI has the potential of tailoring a uniquely entertaining and meaningful game experience to a specific player. An online adaptive AI should be able to profile its opponent efficiently during the early phase of the game and adapts its own playing style to the level of the player so that the player feels entertained playing against it. This paper presents an online adaptive algorithm that uses ideas from evolutionary computation to match the skill level of the opponent during the game. The proposed algorithms demonstrated using a car racing simulator is capable of matching its opponents in terms of both mean score and winning percentages.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115430402","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}
引用次数: 19
Combining LPP with PCA for microarray data clustering 结合LPP和PCA的微阵列数据聚类
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631074
Chuanliang Chen, R. Bie, Ping Guo
{"title":"Combining LPP with PCA for microarray data clustering","authors":"Chuanliang Chen, R. Bie, Ping Guo","doi":"10.1109/CEC.2008.4631074","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631074","url":null,"abstract":"DNA microarray technique has produced large amount of gene expression data. To analyze these data, many excellent machine learning techniques have been proposed in recent related work. In this paper, we try to perform the clustering of microarray data by combining the recently proposed locality preserving projection (LPP) method with PCA, i.e. PCA-LPP. The comparison between PCA and PCA-LPP is performed based on two clustering algorithms, K-means and agglomerative hierarchical clustering. As we already known, clustering with the components extracted by PCA instead of the original variables does improve cluster quality. Moreover, our empirical study shows that by using LPP to perform further process the dimensions of components extracted by PCA can be further reduced and the quality of the clusters can be improved greatly meanwhile. Particularly, the first few components obtained by PCA-LPP capture more information of the cluster structure than those of PCA.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115712075","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}
引用次数: 2
Algorithm comparisons and the significance of population size 算法比较和种群大小的意义
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630905
K. Malan, A. Engelbrecht
{"title":"Algorithm comparisons and the significance of population size","authors":"K. Malan, A. Engelbrecht","doi":"10.1109/CEC.2008.4630905","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630905","url":null,"abstract":"In studies that compare the performance of population-based optimization algorithms, it is sometimes assumed that the comparison is valid as long as the number of function evaluations is equal, even if the population size differs. This paper shows that such comparisons are invalid. The performance of two algorithms: differential evolution (DE) and global best particle swarm optimization (gbest PSO) are tested on standard benchmark problems with different numbers of individuals/particles (20, 50 and 100). It is shown that there are significance differences in the performance of the same algorithm with the same number of function evaluations, but with different numbers of individuals/particles. Comparisons of different algorithms should therefore always use the same population size for results to be valid.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116897712","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}
引用次数: 12
Learning benefits evolution if sex gives pleasure 如果性能带来快乐，学习有利于进化
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631073
R. Griffioen, S. Smit, A. Eiben
{"title":"Learning benefits evolution if sex gives pleasure","authors":"R. Griffioen, S. Smit, A. Eiben","doi":"10.1109/CEC.2008.4631073","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631073","url":null,"abstract":"In this paper the effects of individual learning on an evolving population of situated agents are investigated. We work with a novel type of system where agents can decide autonomously (by their controllers) if/when they reproduce and the bias in the agent controllers for the mating action is adaptable by individual learning. Our experiments show that in such a system reinforcement learning with the straightforward rewards system based on energy makes the agents lose their interest in mating. In other words, we see that learning frustrates evolution, killing the whole population on the long run. This effect can be counteracted by introducing a specially designated positive mating reward, pretty much like an orgasm in Nature. With this twist individual learning becomes a positive force. It can make the otherwise disappearing population viable by keeping agents alive that did not yet learn the task at hand. This hiding effect proves positive for it provides a smooth road for the population to adapt and learn the task with a lower risk of extinction.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117188405","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}
引用次数: 2
Convergence analysis of the Brain-state-in-a-Box(BSB) model with delay 脑-状态-盒(BSB)模型的收敛性分析
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631070
X. Qiu, S. Qiu
{"title":"Convergence analysis of the Brain-state-in-a-Box(BSB) model with delay","authors":"X. Qiu, S. Qiu","doi":"10.1109/CEC.2008.4631070","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631070","url":null,"abstract":"In this paper, theoretical analysis proves the convergence properties of the brain-state-in-a-box (BSB) models with delay. We propose a convergence theorem of the BSB with delay, generalized the BSB without delay, while all previous studies on this model without delay assumed that symmetric and quasi-symmetric. We have performed a detailed convergence analysis of this network and found convergence theorem under proper assumptions of the weight matrices of this network. One is non-symmetric and the other is row diagonal dominant. Meanwhile, the updating process is presented by a newly given updating rule. Theoretical analysis demonstrates that the BSB with delay performs much better than the original one in updating to an equilibrium point, and its updating rate is four times higher than that of the original BSB.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117200154","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}
引用次数: 1
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