2017 IEEE Congress on Evolutionary Computation (CEC)最新文献

Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems 基于精英群体进化算法的推荐系统信息核心优化

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-07-07 DOI: 10.1109/CEC.2017.7969435

Caihong Mu, Huiwen Cheng, Wei Feng, Yi Liu, R. Qu

{"title":"Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems","authors":"Caihong Mu, Huiwen Cheng, Wei Feng, Yi Liu, R. Qu","doi":"10.1109/CEC.2017.7969435","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969435","url":null,"abstract":"Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users” or “information core” whose historical behavior data are more reliable, objective and positive for making recommendations. Finding the information core is of great interests to greatly increase the speed of online recommendation. There is no general method to identify core users in the existing literatures. In this paper, a general method of finding information core is proposed by modelling this problem as a combinatorial optimization problem. A novel Evolutionary Algorithm with Elite Population (EA-EP) is presented to search for the information core, where an elite population with a new crossover mechanism named as ordered crossover is used to accelerate the evolution. Experiments are conducted on Movielens (100k) to validate the effectiveness of our proposed algorithm. Results show that EA-EP is able to effectively identify core users and leads to better recommendation accuracy compared to several existing greedy methods and the conventional collaborative filter (CF). In addition, EA-EP is shown to significantly reduce the time of online recommendation.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134474246","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

Local Optima Networks of the Permutation Flowshop Scheduling Problem: Makespan vs. total flow time 置换流水车间调度问题的局部最优网络:最大完工时间与总流时间

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-07-07 DOI: 10.1109/CEC.2017.7969541

Leticia Hernando, F. Daolio, Nadarajen Veerapen, G. Ochoa

引用次数: 15

Knowledge-based particle swarm optimization for PID controller tuning 基于知识的粒子群优化PID控制器整定

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-07-07 DOI: 10.1109/CEC.2017.7969522

Junfeng Chen, M. Omidvar, M. Azad, Xin Yao

{"title":"Knowledge-based particle swarm optimization for PID controller tuning","authors":"Junfeng Chen, M. Omidvar, M. Azad, Xin Yao","doi":"10.1109/CEC.2017.7969522","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969522","url":null,"abstract":"A proportional-integral-derivative (PID) controller is a control loop feedback mechanism widely employed in industrial control systems. The parameters tuning is a sticking point, having a great effect on the control performance of a PID system. There is no perfect rule for designing controllers, and finding an initial good guess for the parameters of a well-performing controller is difficult. In this paper, we develop a knowledge-based particle swarm optimization by incorporating the dynamic response information of PID into the optimizer. Prior knowledge not only empowers the particle swarm optimization algorithm to quickly identify the promising regions, but also helps the proposed algorithm to increase the solution precision in the limited running time. To benchmark the performance of the proposed algorithm, an electric pump drive and an automatic voltage regulator system are selected from industrial applications. The simulation results indicate that the proposed algorithm with a newly proposed performance index has a significant performance on both test cases and outperforms other algorithms in terms of overshoot, steady state error, and settling time.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126546255","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: 20

Bus Routing for emergency evacuations: The case of the Great Fire of Valparaiso 紧急疏散的巴士路线:瓦尔帕莱索大火的案例

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-07-05 DOI: 10.1109/CEC.2017.7969589

Javiera Loyola Vitali, M. Riff, Elizabeth Montero

引用次数: 3

New heuristics for multi-objective worst-case optimization in evidence-based robust design 基于证据的稳健设计中多目标最坏情况优化的新启发式方法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-07-05 DOI: 10.1109/CEC.2017.7969483

C. Ortega, M. Vasile

引用次数: 2

The Functional Dendritic Cell Algorithm: A formal specification with Haskell 功能性树突状细胞算法:Haskell的正式规范

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-07 DOI: 10.1109/CEC.2017.7969518

Julie Greensmith, Michael B. Gale

引用次数: 6

A genetic algorithm for the UCITS-constrained index-tracking problem ucits约束下索引跟踪问题的遗传算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-07 DOI: 10.1109/CEC.2017.7969394

O. Strub, N. Trautmann

{"title":"A genetic algorithm for the UCITS-constrained index-tracking problem","authors":"O. Strub, N. Trautmann","doi":"10.1109/CEC.2017.7969394","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969394","url":null,"abstract":"We consider the problem of replicating the returns of a financial index as accurately as possible by selecting a subset of the assets that constitute the index and determining the portfolio weight of each selected asset subject to various constraints that are relevant in practice, including the UCITS III (Undertakings for Collective Investments in Transferable Securities) 5/10/40 concentration rule. For this problem, we present a genetic algorithm, in which the individuals correspond to subsets of the index constituents. The fitness of the individuals is determined by applying mixed-integer quadratic programming. Two main features of the presented genetic algorithm are novel. First, we use a representation of subsets which is the first that exhibits all of the four desirable properties feasibility, efficiency, locality, and heritability. The representation also allows to incorporate problem-specific knowledge in a very simple way. Second, to reduce the CPU time for the fitness evaluations, we first estimate the fitness of the individuals in an efficient way and then evaluate the fitness of promising individuals only. The results of a computational experiment based on real-world data demonstrate that in particular for large instances, the presented genetic algorithm devises very good solutions in short CPU time.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134429971","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

Learning heuristic selection using a Time Delay Neural Network for Open Vehicle Routing 基于时滞神经网络的开放式车辆路径学习启发式选择

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-06 DOI: 10.1109/CEC.2017.7969477

R. Tyasnurita, E. Özcan, R. John

{"title":"Learning heuristic selection using a Time Delay Neural Network for Open Vehicle Routing","authors":"R. Tyasnurita, E. Özcan, R. John","doi":"10.1109/CEC.2017.7969477","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969477","url":null,"abstract":"A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heuristic for Open Vehicle Routing Problem (OVRP). As a chosen ‘expert’ hyper-heuristic is run on a small set of training problem instances, data is collected to learn from the expert regarding how to decide which low-level heuristic to select and apply to the solution in hand during the search process. In this study, a Time Delay Neural Network (TDNN) is used to extract hidden patterns within the collected data in the form of a classifier, i.e an ‘apprentice’ hyper-heuristic, which is then used to solve the ‘unseen’ problem instances. Firstly, the parameters of TDNN are tuned using Taguchi orthogonal array as a design of experiments method. Then the influence of extending and enriching the information collected from the expert and fed into TDNN is explored on the behaviour of the generated apprentice hyper-heuristic. The empirical results show that the use of distance between solutions as an additional information collected from the expert generates an apprentice which outperforms the expert algorithm on a benchmark of OVRP instances.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132959029","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: 39

Parameter estimation of nonlinear nitrate prediction model using genetic algorithm 基于遗传算法的非线性硝酸盐预测模型参数估计

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969532

Rui Wu, Jose T. Painumkal, J. Volk, Siming Liu, S. Louis, S. Tyler, S. Dascalu, F. Harris

引用次数: 10

Evolving Deep Neural Networks architectures for Android malware classification 基于Android恶意软件分类的进化深度神经网络架构

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969501

Alejandro Martín, Félix Fuentes-Hurtado, V. Naranjo, David Camacho

{"title":"Evolving Deep Neural Networks architectures for Android malware classification","authors":"Alejandro Martín, Félix Fuentes-Hurtado, V. Naranjo, David Camacho","doi":"10.1109/CEC.2017.7969501","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969501","url":null,"abstract":"Deep Neural Networks (DNN) have become a powerful, widely used, and successful mechanism to solve problems of different nature and varied complexity. Their ability to build models adapted to complex non-linear problems, have made them a technique widely applied and studied. One of the fields where this technique is currently being applied is in the malware classification problem. The malware classification problem has an increasing complexity, due to the growing number of features needed to represent the behaviour of the application as exhaustively as possible. Although other classification methods, as those based on SVM, have been traditionally used, the DNN pose a promising tool in this field. However, the parameters and architecture setting of these DNNs present a serious restriction, due to the necessary time to find the most appropriate configuration. This paper proposes a new genetic algorithm designed to evolve the parameters, and the architecture, of a DNN with the goal of maximising the malware classification accuracy, and minimizing the complexity of the model. This model is tested against a dataset of malware samples, which are represented using a set of static features, so the DNN has been trained to perform a static malware classification task. The experiments carried out using this dataset show that the genetic algorithm is able to select the parameters and the DNN architecture settings, achieving a 91% accuracy.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"21 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":"123707858","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: 28