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2010 UK Workshop on Computational Intelligence (UKCI)最新文献

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Planning using online evolutionary overfitting 利用在线进化过拟合进行规划
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625569
Spyridon Samothrakis, S. Lucas
{"title":"Planning using online evolutionary overfitting","authors":"Spyridon Samothrakis, S. Lucas","doi":"10.1109/UKCI.2010.5625569","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625569","url":null,"abstract":"Biological systems tend to perform a range of tasks of extreme variability with extraordinary efficiency. It has been argued that a plausible scenario for achieving such versatility is explicitly learning a forward model. We perform a set of experiments using the original and a modified version of a classic reinforcement learning task, the mountain car problem, using a number of agents that encode both a direct and an abstracted version of a forward model. The results suggest that superior performance can be achieved if the forward model can be exploited in real-time by an agent that has already internalised a model-free control function.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124172297","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: 4
Application of evolutionary algorithms to learning evolved Bayesian Network models of rig operations in the Gulf of Mexico 应用进化算法学习墨西哥湾钻井作业的进化贝叶斯网络模型
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625588
François A. Fournier, Yanghui Wu, J. Mccall, Andrei V. Petrovski, P. Barclay
{"title":"Application of evolutionary algorithms to learning evolved Bayesian Network models of rig operations in the Gulf of Mexico","authors":"François A. Fournier, Yanghui Wu, J. Mccall, Andrei V. Petrovski, P. Barclay","doi":"10.1109/UKCI.2010.5625588","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625588","url":null,"abstract":"The operation of drilling rigs is highly expensive. It is therefore important to be able to identify and analyse variables affecting rig operations. We investigate the use of Genetic Algorithms and Ant Colony Optimisation to induce a Bayesian Network model for the real world problem of Rig Operations Management and confirm the validity of our previous model. We explore the relative performances of different search and scoring heuristics and consider trade-offs between best network score and computation time from an industry standpoint. Finally, we analyse edge-discovery statistics over repeated runs to explain observed differences between the algorithms.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165439","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: 4
Evolutionary Programming using a mixed strategy with incomplete information 采用不完全信息混合策略的进化规划
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625571
Liang Shen, Jun He
{"title":"Evolutionary Programming using a mixed strategy with incomplete information","authors":"Liang Shen, Jun He","doi":"10.1109/UKCI.2010.5625571","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625571","url":null,"abstract":"Evolutionary Programming (EP) has been modified in various ways. In particular, modifications of the mutation operator have been proved to be capable of significantly improving the performance of EP. However, while each of proposed mutation operators (e.g. Gaussian mutation and Cauchy mutation) may be suitable for solving certain types of problem, none of them are suitable for all problems. Mixed strategies have therefore been proposed in order to combine the advantages of different operators. The design of a mixed strategy is currently based on the premise that complete and perfect information is held for each mutation operator in the mixed strategy such that the payoff functions to each pure strategy are common knowledge. This paper presents a modified mixed strategy (IMEP) involving a process with incomplete information. Experimental results show that IMEP outperforms pure strategy algorithms in spite of the lack of information. The experiments also show that the results are similar to those generated by the original algorithm, which was complete information.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115299134","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
Heuristic portfolio optimisation for a hedge fund strategy using the Geometric Nelder-Mead Algorithm 基于几何Nelder-Mead算法的对冲基金启发式投资组合优化
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625577
A. Alentorn, A. Moraglio, Colin Johnson
{"title":"Heuristic portfolio optimisation for a hedge fund strategy using the Geometric Nelder-Mead Algorithm","authors":"A. Alentorn, A. Moraglio, Colin Johnson","doi":"10.1109/UKCI.2010.5625577","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625577","url":null,"abstract":"This paper presents a framework for heuristic portfolio optimisation applied to a hedge fund investment strategy. The first contribution of the paper is to present a framework for implementing portfolio optimisation of a market neutral hedge fund strategy. The paper also illustrates the application of the recently developed Geometric Nelder-Mead Algorithm (GNMA) in solving this real world optimization problem, compared with a Genetic Algorithm (GA) approach.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124271834","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
Extreme learning machine for mammographie risk analysis 用于乳房x光检查风险分析的极限学习机
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625590
Yanpeng Qu, Qiang Shen, N. M. Parthaláin, Wei Wu
{"title":"Extreme learning machine for mammographie risk analysis","authors":"Yanpeng Qu, Qiang Shen, N. M. Parthaláin, Wei Wu","doi":"10.1109/UKCI.2010.5625590","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625590","url":null,"abstract":"The assessment of mammographie risk analysis is an important issue in the medical field. Various approaches have been applied in order to achieve a higher accuracy in such analysis. In this paper, an approach known as Extreme Learning Machines (ELM), is employed to generate a single hidden layer neural network based classifier for estimating mammographie risk. ELM is able to avoid problems such as local minima, improper learning rate, and overfitting which iterative learning methods tend to suffer from. In addition the training phase of ELM is very fast. The performance of the ELM-trained neural network is compared with a number of state of the art classifiers. The results indicate that the use of ELM entails better classification accuracy for the problem of mammographie risk analysis.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123826293","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: 4
Solving a practical clustering problem via GTMAS 利用GTMAS解决一个实际的聚类问题
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625574
Ozgun Toreyen, A. Salhi
{"title":"Solving a practical clustering problem via GTMAS","authors":"Ozgun Toreyen, A. Salhi","doi":"10.1109/UKCI.2010.5625574","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625574","url":null,"abstract":"The Game Theory-basedMulti-Agent System (GTMAS) of Salhi and Töreyen, [10] and [12], implements a loosely coupled hybrid algorithm that may involve any number of algorithms suitable, a priori, for the solution of a given optimisation problem. The system allows the available algorithms to cooperate toward the solution of the problem in hand as well as compete for the computing facilities they require to run. This co-operative/competitive aspect is captured through the implementation of the Prisoners' Dilemma paradigm of game theory.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130631926","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
Deterministic parameter control in Harmony Search 和声搜索中的确定性参数控制
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625576
R. Diao, Q. Shen
{"title":"Deterministic parameter control in Harmony Search","authors":"R. Diao, Q. Shen","doi":"10.1109/UKCI.2010.5625576","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625576","url":null,"abstract":"Harmony search is a recently developed meta heuristic capable of solving discrete and continuous valued optimisation problems. However, the nature of pre-defined constant parameters limits the exploitation of the algorithm. This paper proposes a number of deterministic parameter control rules to fine-tune these parameters individually and dynamically, making Harmony Search a more dynamic algorithm which is able to achieve better results. A combined approach that implements all the proposed rules is then applied to various benchmarks and engineering problems. Experimental results reveal that the combined approach can find better solutions when compared to the original harmony search and several other heuristics, making harmony search a strong mechanism to perform optimisation tasks.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132183311","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: 7
Soft computing head tracking interaction for telerobotic control 用于遥控机器人控制的软计算头部跟踪交互
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625587
Stephen Vickers, S. Coupland
{"title":"Soft computing head tracking interaction for telerobotic control","authors":"Stephen Vickers, S. Coupland","doi":"10.1109/UKCI.2010.5625587","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625587","url":null,"abstract":"A great deal of research is currently taking place in the area of assistive technologies. One part of this work looks at creating a range of interaction techniques for a variety of input devices to make technology more accessible to motor impaired users. This paper examines a head tracking input device for control of a small mobile robot. Two interfaces are compared, the first based on existing work using eye gaze input devises and the second uses a fuzzy logic system to create the interface which is built from a common sense set of rules. Although no statistically significant conclusions can be draw, the results of our pilot study suggest our methodology is helpful when building such interfaces.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125772649","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
Predictive model representation and comparison: Towards data and predictive models governance 预测模型表示和比较:面向数据和预测模型治理
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625573
M. Makhtar, D. Neagu, M. Ridley
{"title":"Predictive model representation and comparison: Towards data and predictive models governance","authors":"M. Makhtar, D. Neagu, M. Ridley","doi":"10.1109/UKCI.2010.5625573","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625573","url":null,"abstract":"The increasing variety of data mining tools offers a large palette of types and representation formats for predictive models. Managing the models becomes then a big challenge, as well as reusing the models and keeping the consistency of model and data repositories because of the lack of an agreed representation across the models. The flexibility of XML representation makes it easier to provide solutions for Data and Model Governance (DMG) and support data and model exchange. We choose Predictive Toxicology as an application field to demonstrate our approach to represent predictive models linked to data for DMG. We propose an original structure: Predictive Toxicology Markup Language (PTML) offers a representation scheme for predictive toxicology data and models generated by data mining tools. We also show how this representation offers possibilities to compare models by similarity using our Distance Models Comparison technique. This work is ongoing and first encouraging results for calculating PTML distance are reported hereby.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129146951","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: 7
Bayesian Decision Trees for EEG Assessment of newborn brain maturity 新生儿脑成熟度脑电评估的贝叶斯决策树
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625584
L. Jakaite, V. Schetinin, C. Maple, J. Schult
{"title":"Bayesian Decision Trees for EEG Assessment of newborn brain maturity","authors":"L. Jakaite, V. Schetinin, C. Maple, J. Schult","doi":"10.1109/UKCI.2010.5625584","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625584","url":null,"abstract":"Decision Tree (DT) models are observable for clinical experts and can be used for a probabilistic inference within Bayesian Model Averaging (BMA). The use of Markov Chain Monte Carlo (MCMC) search makes the BMA computationally practical. We employ the MCMC BMA strategy for assessing newborn brain maturity from clinical EEG. Our analysis has revealed that an appreciable part of EEG features is rarely used in the DT models, because these features make weak contribution to the assessment. It was also found that the portion of DT models using weak EEG features was large. On one side, this obstructs interpretation of DT models. On the other side, weak attributes increase dimensionality of a model parameter space that MCMC needs to explore in detail. We assume that discarding the DT models using weak features will reduce these negative impacts. Specifically, in this paper we explore the influence of pruning DTs on the results obtained within the discarding technique we proposed. Our experiments have shown that, given a pruning factor, the original set of EEG features can be greatly reduced without a decrease in accuracy of assessment.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834224","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: 18
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