2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)最新文献

Recurrent neural network ensembles for convergence prediction in surrogate-assisted evolutionary optimization 递归神经网络集成在代理辅助进化优化中的收敛预测

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595766

Colin P. Smith, John James Doherty, Yaochu Jin

{"title":"Recurrent neural network ensembles for convergence prediction in surrogate-assisted evolutionary optimization","authors":"Colin P. Smith, John James Doherty, Yaochu Jin","doi":"10.1109/CIDUE.2013.6595766","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595766","url":null,"abstract":"Evaluating the fitness of candidate solutions in evolutionary algorithms can be computationally expensive when the fitness is determined using an iterative numerical process. This paper illustrates how an ensemble of Recurrent Neural Networks can be used as a robust surrogate to predict converged Computational Fluid Dynamics data from unconverged data. The training of the individual neural networks is controlled and a variance range is used to determine if the surrogates have been adequately trained to predict diverse and accurate solutions. Heterogeneous ensemble members are used due to the limited data available and results show that for certain parameters, predictions can be made to within 5% of the converged data's final output, using approximately 40% of the iterations needed for convergence. The implications of the method and results presented are that it is possible to use ensembles of Recurrent Neural Networks to provide accurate fitness predictions for an evolutionary algorithm and that they could be used to reduce the time needed to achieve optimal designs based on time-consuming Computational Fluid Dynamics simulations.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125320651","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

A modular technique for monthly rainfall time series prediction 月降雨时间序列预测的模块化技术

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595775

Jesada Kaiornrit, Kok Wai Wong, C. Fung

{"title":"A modular technique for monthly rainfall time series prediction","authors":"Jesada Kaiornrit, Kok Wai Wong, C. Fung","doi":"10.1109/CIDUE.2013.6595775","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595775","url":null,"abstract":"Rainfall time series forecasting is a crucial task in water resource planning and management. Conventional time series prediction models and intelligent models have been applied to this task. Attempt to develop better models is an ongoing endeavor. Besides accuracy, the transparency and practicality of the model are the other important issues that need to be considered. To address these issues, this study proposes the use of a modular technique to a monthly rainfall time series prediction model. The proposed model consists of two main layers, namely, a prediction layer and an aggregation layer. In the prediction layer, Mamdani-type fuzzy inference system is used to capture the input-output relationship of the rainfall pattern. In the aggregation layer, Bayesian learning and nonlinear programming are used to capture the uncertainty in the time dimension. Eight monthly rainfall time series collected from the northeast region of Thailand are used to evaluate the proposed model. The experimental results showed that the proposed model could improve the prediction accuracy from the single model. Furthermore, human analysts can interpret such model as it contains set of fuzzy rules.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123477576","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: 5

A memetic algorithm for dynamic economic load dispatch optimization 动态经济负荷调度优化的模因算法

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595777

S. Orike, D. Corne

{"title":"A memetic algorithm for dynamic economic load dispatch optimization","authors":"S. Orike, D. Corne","doi":"10.1109/CIDUE.2013.6595777","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595777","url":null,"abstract":"The dynamic economic load dispatch (DELD) problem is an extension of the conventional static load dispatch problem in the context of electrical power generation. In the static case, the problem is to optimize the settings for each unit in a generating station so as to supply sufficient power to meet a given overall predicted demand for minimal cost. In the dynamic version of the problem, predicted demand exists for each of a number of successive periods (e.g. 24 hourly periods), and the static version of the problem is to be solved for each period. Until now, the DELD has been treated as a series of static problems. In this paper, we take a memetic algorithm (MA) that has recently provided superior results on some benchmark problems for the static ELD, and we now adapt it for the dynamic case, and investigate a simple dynamic optimization approach to this where the final population of a previous period is used to intialise the population for the next period. This is compared with two baselines, in which (i) the static problems are solved independently, and (ii) the static problems are solved together, treated as a single multi-part problem with suitably adjusted constraints. We evaluate our methods on two benchmark cases of the DELD for which published results exist, and we show that the basic dynamic optimization approach, using our MA, has superior performance to both the baseline approaches and to other approaches published in the literature so far.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124597642","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: 8

Dynamic significant feature extraction for embedded intelligent agent implementations 嵌入式智能代理实现的动态显著特征提取

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595770

Koldo Basterretxea, I. del Campo, Maria Victoria Martinez, J. Echanobe

{"title":"Dynamic significant feature extraction for embedded intelligent agent implementations","authors":"Koldo Basterretxea, I. del Campo, Maria Victoria Martinez, J. Echanobe","doi":"10.1109/CIDUE.2013.6595770","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595770","url":null,"abstract":"“Autonomy” and “adaptability” are key features of intelligent systems with environment awareness. Many applications of intelligent agents require the processing of information coming in from many available sensors to produce adequate output responses in changing scenarios. For such applications, the concept of autonomy should apply not only to the ability of the agent to produce correct outputs without human guidance, but also to its potential ubiquity and portability. However, processing complex computational intelligence algorithms in small, low-power embedded systems, very often with tight delay constraints, is a challenging engineering problem. In this paper a computationally efficient neuro-fuzzy information processing paradigm is tested in an ambient intelligent scenario to evaluate its appropriateness for future embedded SoC (System on Chip) implementations. The system has been endowed with an information preprocessing module based on Principal Component Analysis (PCA) that produces reduced input space dimensionalities with little loss of modeling power. An eventual on-chip PCA module could be applied to dynamically update the reduced meaningful space of information from the outside world. Moreover, the applicability of the PCA module to obtain a fault-tolerant agent in the presence of sensor failures has also been investigated with satisfactory results.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"21 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261915","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

Surrogate enhanced interactive genetic algorithm with weighted Gaussian process 加权高斯过程代理增强交互式遗传算法

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595769

Shanshan Chen, Xiaoyan Sun, D. Gong, Yong Zhang

{"title":"Surrogate enhanced interactive genetic algorithm with weighted Gaussian process","authors":"Shanshan Chen, Xiaoyan Sun, D. Gong, Yong Zhang","doi":"10.1109/CIDUE.2013.6595769","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595769","url":null,"abstract":"Interactive genetic algorithm (IGA), combining a user's intelligent evaluation with the traditional operators of genetic algorithms, are developed to optimize those problems with aesthetic indicators. The evaluation uncertainties and burden, however, greatly restrict the applications of IGA in complicated situations. Surrogate model approximating to the evaluation of the user has been generally applied to alleviate the evaluation burden of the user. The evaluation uncertainties, however, are not taken into account in existing research, therefore, a weighted multi-output gaussian process is here proposed to build the surrogate model by incorporating the uncertainty so as to enhance the performance of IGA. First, an IGA with interval fitness evaluation is adopted to depict the evaluation uncertainty, and the evaluation noise is defined based on the assignment. With the evaluation noise, the weight of each training sample is calculated and used to train a gaussian process which has two outputs to approximate the upper and lower values of the interval fitness, respectively. The trained gaussian process is treated as a fitness function and used to estimate the fitness of individuals generated in the subsequent evolutions. The proposed algorithm is applied to a benchmark function and a real-world fashion design to experimentally demonstrate its strength in searching.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114305305","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

Discounted expert weighting for concept drift 概念漂移的贴现专家权重

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595773

G. Ditzler, G. Rosen, R. Polikar

{"title":"Discounted expert weighting for concept drift","authors":"G. Ditzler, G. Rosen, R. Polikar","doi":"10.1109/CIDUE.2013.6595773","DOIUrl":"https://doi.org/10.1109/CIDUE.2013.6595773","url":null,"abstract":"Multiple expert systems (MES) have been widely used in machine learning because of their inherent ability to decrease variance and improve generalization performance by receiving advice from more than one expert. However, a typical MES explicitly assumes that training and testing data are independent and identically distributed (iid), which, unfortunately, is often violated in practice when the probability distribution generating the data changes with time. One of the key aspects of any MES algorithm deployed in such environments is the decision rule used to combine the decisions of the experts. Many MES algorithms choose adaptive weighting schemes that adjust the weights of a classifier based on its loss in recent time, or use an average of the experts probabilities. However, in a stochastic setting where the loss of an expert is uncertain at a future point in time, which combiner method is the most reliable? In this work, we show that non-uniform weighting experts can provide a stable upper bound on loss compared to techniques such as a follow-the-Ieader or uniform methodology. Several well-studied MES approaches are tested on a variety of real-world data sets to support and demonstrate the theory.","PeriodicalId":133590,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115411288","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: 8

Co-evolutionary learning in the n-choice iterated prisoner's dilemma with PSO algorithm in a spatial environment 空间环境下基于粒子群算法的n选择迭代囚徒困境协同进化学习

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595771

Xiaoyang Wang, Huiyou Chang, Yang Yi, Yibin Lin

引用次数: 5

Benchmarks for dynamic multi-objective optimisation 动态多目标优化的基准

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595776

Mardé Helbig, A. Engelbrecht

引用次数: 36

Takeover time in dynamic optimization problems 动态优化问题中的接管时间

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595768

Yesnier Bravo, Gabriel Luque, E. Alba

引用次数: 2

A Bayesian network model for evacuation time analysis during a ship fire 船舶火灾疏散时间分析的贝叶斯网络模型

2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE) Pub Date : 2013-04-16 DOI: 10.1109/CIDUE.2013.6595778

Parvaneh Sarshar, Jaziar Radianti, Ole-Christoffer Granmo, Jose J. Gonzalez

引用次数: 19