2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第9页

Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system 进气通风系统的代理辅助进化多目标形状优化

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969486

Tinkle Chugh, Karthik Sindhya, K. Miettinen, Yaochu Jin, T. Krátký, P. Makkonen

{"title":"Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system","authors":"Tinkle Chugh, Karthik Sindhya, K. Miettinen, Yaochu Jin, T. Krátký, P. Makkonen","doi":"10.1109/CEC.2017.7969486","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969486","url":null,"abstract":"We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. From the set of nondominated solutions generated by K-RVEA, a decision maker having substance knowledge selected the final one based on his preferences. The final selected solution has better objective function values compared to the baseline solution of the initial design. A comparison of solutions with K-RVEA and RVEA (which does not use surrogates) is also performed to show the potential of using surrogates.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115980683","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: 31

A Random Key based Estimation of Distribution Algorithm for the Permutation Flowshop Scheduling Problem 基于随机密钥估计的置换流水车间调度算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969591

M. Ayodele, J. Mccall, Olivier Regnier-Coudert, Liam Bowie

引用次数: 10

Engineering benchmark generation and performance measurement of evolutionary algorithms 演化算法的工程基准生成与性能测量

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969380

Joon-Hoon Kim, Ho Min Lee, Donghwi Jung, Ali Sadollah

引用次数: 5

Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution 基于方差的无收敛奖励估计学习分类器系统在各种奖励分布中的应用

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969625

Takato Tatsumi, Hiroyuki Sato, T. Kovacs, K. Takadama

{"title":"Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution","authors":"Takato Tatsumi, Hiroyuki Sato, T. Kovacs, K. Takadama","doi":"10.1109/CEC.2017.7969625","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969625","url":null,"abstract":"This paper focuses on a generalization of classifiers in noisy problems and aims at exploring learning classifier systems (LCSs) that can evolve accurately generalized classifiers as an optimal solution in several environments which include different type of noise. For this purpose, this paper employs XCS-CRE (XCS without Convergence of Reward Estimation) which can correctly identify classifiers as either accurate or inaccurate ones even in a noisy problem, and investigates its effectiveness in several noisy problems. Through intensive experiments of three LCSs (i.e., XCS as the conventional LCS, XCS-SAC (XCS with Self-adaptive Accuracy Criterion) as our previous LCS, and XCS-CRE) on the noisy 11-multiplexer problem where reward value changes according to (a) Gaussian distribution, (b) Cauchy distribution, or (c) Lognormal distribution, the following implications have been revealed: (1) the correct rate of the classifier of XCS-CRE and XCS-SAC converge to 100% in all three types of the reward distribution while that of XCS cannot reach 100%; (2) the population size of XCS-CRE is smallest followed by that of XCS-SAC and XCS; and (3) the percentage of the acquired optimal classifiers of XCS-CRE is highest followed by that of XCS-SAC and XCS.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126760506","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

Weighted Manhattan Distance Classifier; SELDI data for Alzheimer's disease diagnosis 加权曼哈顿距离分类器;阿尔茨海默病诊断的SELDI数据

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969321

Oriehi Edisemi Destiny Anyaiwe, Gautam B. Singh, G. Wilson, T. Geddes

{"title":"Weighted Manhattan Distance Classifier; SELDI data for Alzheimer's disease diagnosis","authors":"Oriehi Edisemi Destiny Anyaiwe, Gautam B. Singh, G. Wilson, T. Geddes","doi":"10.1109/CEC.2017.7969321","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969321","url":null,"abstract":"Mass Spectrometry (Surface Enhanced Laser Desorption Time of Flight (SELDI-TOF) assay technique) for proteomics is based on the consistency and reproducibility of protein/peptide expressions. In this study, we opine that mining collections of mass spectra data instead of detailed study of individual ions generated in the course of Mass Spectrometer assay process, will generate discriminative factors for the diagnosis of Alzheimer's Disease (and other diseases in general). This model; Weighted Manhattan Distance Classifier (WMDC), classifies a test vector to the stage label of the most significant train vector to it using Manhattan Distance function and thereafter, classifies a test data point (a collection of test vectors) to the disease stage having the majority of most significant train vectors in it. The disease severity is categorized as normal/control, mild and acute impaired stages, each of which contained 20 SELDI-TOF analysis results. In all, the database contained 60 assay results of saliva analytes or protein source samples under 3 proteinChips; CM10, IMAC30 and Q10. Each laboratory experiment was performed with either low (1800 nJ) or high (4000 nJ) laser energy bombardment level. 90% classification result was obtained with a probability of 0.075 for committing type II error (that is, a test power of 0.925).","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127141753","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

Solving binary classification problems with carbon nanotube / liquid crystal composites and evolutionary algorithms 碳纳米管/液晶复合材料的二元分类问题及进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969536

E. Vissol-Gaudin, A. Kotsialos, M. K. Massey, C. Groves, C. Pearson, D. Zeze, M. Petty

引用次数: 6

Estimation of distribution algorithms for the Multi-Mode Resource Constrained Project scheduling problem 多模式资源约束项目调度问题的分布算法估计

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969491

M. Ayodele, J. Mccall, Olivier Regnier-Coudert

引用次数: 10

Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem 基于多样性的自适应遗传算法求解劳动力调度和路由问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969516

H. Algethami, Dario Landa Silva

引用次数: 13

A multi-agent genetic algorithm for multi-period emergency resource scheduling problems in uncertain traffic network 不确定交通网络中多周期应急资源调度问题的多智能体遗传算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969294

Yawen Zhou, Jing Liu

引用次数: 2

Automated design of hyper-heuristics components to solve the PSP problem with HP model 超启发式组件的自动化设计，解决HP模型的PSP问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969526

Vidal D. Fontoura, A. Pozo, Roberto Santana

{"title":"Automated design of hyper-heuristics components to solve the PSP problem with HP model","authors":"Vidal D. Fontoura, A. Pozo, Roberto Santana","doi":"10.1109/CEC.2017.7969526","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969526","url":null,"abstract":"The Protein Structure Prediction (PSP) problem is one of the modern most challenging problems from science. Simplified protein models are usually applied to simulate and study some characteristics of the protein folding process. Hence, many heuristic strategies have been applied in order to find simplified protein structures in which the protein configuration has the minimal energy. However, these strategies have difficulties in finding the optimal solutions to the longer sequences of amino-acids, due to the complexity of the problem and the huge amount of local optima. Hyper heuristics have proved to be useful in this type of context since they try to combine different heuristics strengths into a single framework. However, there is lack of work addressing the automated design of hyper-heuristics components. This paper proposes GEHyPSP, an approach which aims to achieve generation, through grammatical evolution, of selection mechanisms and acceptance criteria for a hyper-heuristic framework applied to PSP problem. We investigate the strengths and weaknesses of our approach on a benchmark of simplified protein models. GEHyPSP was able to reach the best known results for 7 instances from 11 that composed the benchmark set used to evaluate the approach.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132240556","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