2018 IEEE Congress on Evolutionary Computation (CEC)最新文献_第5页

Evolutionary Deep Learning: A Genetic Programming Approach to Image Classification 进化深度学习:一种图像分类的遗传规划方法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477933

Benjamin Patrick Evans, Harith Al-Sahaf, Bing Xue, Mengjie Zhang

引用次数: 30

Parallel Multi-Island Genetic Algotirth for Sorting Unsigned Genomes by Reversals 反向排序无符号基因组的平行多岛遗传算法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477968

L. A. D. Silveira, J. L. Soncco-Álvarez, T. Lima, M. Ayala-Rincón

{"title":"Parallel Multi-Island Genetic Algotirth for Sorting Unsigned Genomes by Reversals","authors":"L. A. D. Silveira, J. L. Soncco-Álvarez, T. Lima, M. Ayala-Rincón","doi":"10.1109/CEC.2018.8477968","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477968","url":null,"abstract":"Sorting unsigned permutations by reversals is an NP-hard optimization problem with applications in computational molecular biology. Several approximation and metaheuristic algorithms were proposed, among them, in a previous work, a competitive genetic algorithm and its parallel version using island models were proposed. In this paper, focusing on improving accuracy, new island models are proposed by diversifying the distribution of genetic material between islands through static and dynamic communication topologies. In static topologies, communication between islands is predefined and maintained during the computation, while in dynamic topologies the communication is continuously modified. The proposed island models use parallelism in a global and a local level, in which respectively, the exchange of individuals between islands and the fitness computation occurs. Results from the experiments performed with randomly generated synthetic permutations show that parallel island models using both dynamic and static communication topologies outperform parallel approaches found in the literature in terms of run-time as well as accuracy.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124888350","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

Interleaved Cellular Automata, Evolved Artwork and Packing Problems 交错元胞自动机、演化艺术与包装问题

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477972

G. Greenfield

引用次数: 4

Guided Genetic Algorithm for Information Diffusion Problems 信息扩散问题的导向遗传算法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477835

P. Krömer, J. Nowaková

{"title":"Guided Genetic Algorithm for Information Diffusion Problems","authors":"P. Krömer, J. Nowaková","doi":"10.1109/CEC.2018.8477835","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477835","url":null,"abstract":"Information diffusion is a process that involves the propagation of an arbitrary signal (message) in an environment. In the area of social networks, it is often associated with influence maximization. Influence maximization consists in the search for an optimum set of $k$ network nodes (seed sets) that trigger the activation of a maximum total number of remaining network nodes according to a chosen propagation model. It is an attractive research topic due to its well–known difficulty and many practical applications. Influence maximization can be used in various areas spanning from social network analysis and data mining to practical applications such as viral marketing and opinion making. Formally, it can be formulated as a subset selection problem. Because of the proven hardness of the influence maximization problem, many metaheuristic and evolutionary methods have been proposed to tackle it. This paper presents and evaluates a new genetic algorithm for influence maximization. It is based on a recent genetic algorithm for fixed–length subset selection and takes advantage of the knowledge of the environment. The evolutionary algorithm is in this approach executed with respect to network properties and the probability that vertices with chosen properties are selected is increased. The experiments show that this approach improves the results of the evolutionary procedure and leads to the discovery of better seed sets.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129514798","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: 3

Computational Strategy to Predict Possible Protein Function Using an Evolutionary Algorithm Implement in SIFTER Tool 在SIFTER工具中使用进化算法预测可能的蛋白质功能的计算策略

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477982

J. C. Castaneda-Alonso, C. A. Sierra, T. A. Rodriguez, Jonatan Gómez Perdomo

引用次数: 0

Multi-Objective Evolutionary Algorithm with Gaussian Process Regression 基于高斯过程回归的多目标进化算法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477857

E. Guerrero-Peña, A. Araujo

引用次数: 1

Boosting the Performance of MOEA/D-DRA with a Multi-Objective Hyper-Heuristic Based on Irace and UCB Method for Heuristic Selection 基于Irace和UCB启发式选择的多目标超启发式算法提高MOEA/D-DRA性能

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477661

Lucas Prestes, M. Delgado, R. Lüders, Richard A. Gonçalves, C. Almeida

{"title":"Boosting the Performance of MOEA/D-DRA with a Multi-Objective Hyper-Heuristic Based on Irace and UCB Method for Heuristic Selection","authors":"Lucas Prestes, M. Delgado, R. Lüders, Richard A. Gonçalves, C. Almeida","doi":"10.1109/CEC.2018.8477661","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477661","url":null,"abstract":"Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) is one of the most successful decomposition based multiobjective algorithm. Its main feature is a mechanism to allocate different computational effort proportional to the difficult of each subproblem. Despite its success, MOEA/D-DRA has a large set of parameters and operators, whose selection could be a difficult task. This paper aims at improving the performance of MOEA/D-DRA by means of a hyper-heuristic using two parameter/operator selection phases: one off-line strongly based on Iterated Race Automatic Algorithm Configuration (Irace) and another one (online) based on the Upper Confidence Bound (UCB) technique. The proposed approach is compared with the original MOEA/D-DRA, NSGAII and IBEA over 51 instances of 7 well known benchmarks (CEC 2009, GLT, LZ09, MOP, DTLZ, ZDT and WFG). Results show that Irace and UCB are interesting methods to support the hyper-heuristic functioning when selecting parameters/operators of MOEA/D-DRA in the addressed problems.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129980106","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

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction 基于机器学习算法的洪水灾情预测数据科学方法

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477904

Mohammed Khalaf, A. Hussain, D. Al-Jumeily, T. Baker, Robert Keight, P. Lisboa, P. Fergus, Ala S. Al Kafri

{"title":"A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction","authors":"Mohammed Khalaf, A. Hussain, D. Al-Jumeily, T. Baker, Robert Keight, P. Lisboa, P. Fergus, Ala S. Al Kafri","doi":"10.1109/CEC.2018.8477904","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477904","url":null,"abstract":"In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide-scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132067052","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: 30

Maximum Power Point Tracking in Photovoltaic Farms Using DE and PSO Algorithms: A Comparative Study 基于DE和PSO算法的光伏电站最大功率点跟踪的比较研究

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477797

Faizan Khan, A. Sunbul, Mohammad. Y. Ali, Haytham Abdei-Gawad, S. Rahnamayan, V. Sood

{"title":"Maximum Power Point Tracking in Photovoltaic Farms Using DE and PSO Algorithms: A Comparative Study","authors":"Faizan Khan, A. Sunbul, Mohammad. Y. Ali, Haytham Abdei-Gawad, S. Rahnamayan, V. Sood","doi":"10.1109/CEC.2018.8477797","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477797","url":null,"abstract":"Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms are two commonly employed techniques in designing maximum power point tracking systems in photovoltaic (PV) farms. A mathematical formulation of the objective function is derived by implementing the maximum power theorem for load matching using the relationship between input and output impedances. This paper also proposes a novel Center-based Latin Hypercube (CLHS) initialization scheme for population-based algorithms; it is shown that for population initialization, the newly proposed technique of CLHS gives better results with a small population size. A comprehensive comparative study is conducted on DE and PSO algorithms in terms of control parameters, search components, and population initialization methods to determine the best algorithm with its corresponding optimal parameters settings and population initialization to solve a family of maximum power point tracking problems. The work shows that both algorithms are capable of tracking the maximum power point although the PSO is more effective over a small population size. In this study, in overall, 15,876 and 96,228 settings possibilities for DE and PSO respectively are investigated.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132159188","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

An Analysis of Indirect Optimisation Strategies for Scheduling 调度的间接优化策略分析

2018 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2018-07-01 DOI: 10.1109/CEC.2018.8477967

Charles Neau, Olivier Regnier-Coudert, J. Mccall

{"title":"An Analysis of Indirect Optimisation Strategies for Scheduling","authors":"Charles Neau, Olivier Regnier-Coudert, J. Mccall","doi":"10.1109/CEC.2018.8477967","DOIUrl":"https://doi.org/10.1109/CEC.2018.8477967","url":null,"abstract":"By incorporating domain knowledge, simple greedy procedures can be defined to generate reasonably good solutions to many optimisation problems. However, such solutions are unlikely to be optimal and their quality often depends on the way the decision variables are input to the greedy method. Indirect optimisation uses meta-heuristics to optimise the input of the greedy decoders. As the performance and the runtime differ across greedy methods and meta-heuristics, deciding how to split the computational effort between the two sides of the optimisation is not trivial and can significantly impact the search. In this paper, an artificial scheduling problem is presented along with five greedy procedures, using varying levels of domain information. A methodology to compare different indirect optimisation strategies is presented using a simple Hill Climber, a Genetic Algorithm and a population-based Local Search. By assessing all combinations of meta-heuristics and greedy procedures on a range of problem instances with different properties, experiments show that encapsulating problem knowledge within greedy decoders may not always prove successful and that simpler methods can lead to comparable results as advanced ones when combined with meta-heuristics that are adapted to the problem. However, the use of efficient greedy procedures reduces the relative difference between meta-heuristics.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128852665","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