Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)最新文献_第2页

A comparison of neutral landscapes - NK, NKp and NKq 中性景观的比较——NK、NKp和NKq

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006234

N. Geard, Janet Wiles, J. Hallinan, Bradley Tonkes, B. Skellett

{"title":"A comparison of neutral landscapes - NK, NKp and NKq","authors":"N. Geard, Janet Wiles, J. Hallinan, Bradley Tonkes, B. Skellett","doi":"10.1109/CEC.2002.1006234","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006234","url":null,"abstract":"Recent research in molecular evolution has raised awareness of the importance of selective neutrality. Several different models of neutrality have been proposed based on Kauffman's well-known NK landscape model. Two of these models, NKp and NKq, are investigated and found to display significantly different structural properties. The fitness distributions of these neutral landscapes reveal that their levels of correlation with non-neutral landscapes are significantly different, as are the distributions of neutral mutations. In this paper we describe a series of simulations of a hill climbing search algorithm on NK, NKp and NKq landscapes with varying levels of epistatic interaction. These simulations demonstrate differences in the way that epistatic interaction affects the 'searchability' of neutral landscapes. We conclude that the method used to implement neutrality has an impact on both the structure of the resulting landscapes and on the performance of evolutionary search algorithms on these landscapes. These model-dependent effects must be taken into consideration when modelling biological phenomena.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125512114","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: 46

Evolutionary computation approach to Wiener model identification 维纳模型识别的进化计算方法

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1007047

T. Hatanaka, K. Uosaki, M. Koga

引用次数: 16

Comparing a coevolutionary genetic algorithm for multiobjective optimization 比较一种多目标优化的协同进化遗传算法

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004406

J. Lohn, W. Kraus, G. Haith

引用次数: 91

OCR parameters tuning by means of evolution strategies for aircraft's tail number recognition 基于进化策略的飞机尾号识别OCR参数整定

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1007045

A. Berlanga, J. G. Herrero, J. M. Molina, J. Besada, J. Portillo

引用次数: 4

Particle swarm optimisation with spatial particle extension 具有空间粒子扩展的粒子群优化

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004460

T. Krink, J. Vesterstrom, J. Riget

引用次数: 316

Evolutionary adaptive-critic methods for reinforcement learning 强化学习的进化适应批评方法

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004434

Xin Xu, Han-gen He, D. Hu

引用次数: 2

Performance tradeoff between evolutionary computation (EC)/adaptive boosting (AB) hybrid and support vector machine breast cancer classification paradigms 进化计算(EC)/自适应增强(AB)混合算法与支持向量机乳腺癌分类范式的性能权衡

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006231

W. Land, M. Bryden, J. Lo, Daniel W. McKee, F. R. Anderson

{"title":"Performance tradeoff between evolutionary computation (EC)/adaptive boosting (AB) hybrid and support vector machine breast cancer classification paradigms","authors":"W. Land, M. Bryden, J. Lo, Daniel W. McKee, F. R. Anderson","doi":"10.1109/CEC.2002.1006231","DOIUrl":"https://doi.org/10.1109/CEC.2002.1006231","url":null,"abstract":"This paper describes a breast cancer classification performance trade-off analysis using two computational intelligence paradigms. The first, an evolutionary programming (EP)/adaptive boosting (AB) based hybrid, intelligently combines the outputs from an iteratively \"called\" weak learning algorithm (one which performs at least slightly better than random guessing) in order to \"boost\" the performance of an EP-derived weak learner. The second paradigm is support vector machines (SVMs). SVMs are new and radically different types of classifiers and learning machines that use a hypothesis space of linear functions in a high dimensional feature space. The most important advantage of a SVM, unlike neural networks, is that SVM training always finds a global minimum. Furthermore, the SVM has inherent ability to solve pattern classification without incorporating any problem-domain knowledge. In this study, the both the EP/AB hybrid and SVM were employed as pattern classifiers, operating on mammography data used for breast cancer detection. The main focus of the study was to construct and seek the best EP/AB hybrid and SVM configurations for optimum specificity and positive predictive value at very high sensitivities. Using a mammogram database of 500 biopsy-proven samples, the best performing SVM, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 45.0% and a positive predictive value (PPV) of 50.1% at 100% sensitivity. At 97% sensitivity, a specificity of 55.8% and a PPV of 55.2% were obtained. The best performing EP/AB hybrid obtained slightly lower, but comparable, results.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129068837","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: 6

Multi-agent learning via implicit opponent modeling 基于隐式对手建模的多智能体学习

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004470

Ronald V. Bjarnason, T. Peterson

引用次数: 7

Coevolutionary design of Takagi-Sugeno fuzzy systems Takagi-Sugeno模糊系统的协同进化设计

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1004445

M. Delgado, F. von Zuben, F. Gomide

引用次数: 2

Optimising multiple aspects of network survivability 优化网络生存能力的多个方面

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI: 10.1109/CEC.2002.1006219

W. Pullan

引用次数: 8